La mitad de los valores anormales de TSH se normalizan espontáneamente

Meyerovitch J, Rotman-Pikielny P, Sherf M, Battat E, Levy Y, Surks MI. Serum Thyrotropin Measurements in the Community: Five-Year Follow-up in a Large Network of Primary Care Physicians. JAMA Intern Med 2007; 167: 1533-1538.  R   TC (s)   PDF (s)

Introducción

El cribado de la disfunción tiroidea y la necesidad de tratamiento de las disfunciones tiroideas subclínicas son temas controvertidos. Algunos grupos recomiendan un cribado sistemático de la población, pero otros, entre los que se encuentran la US Preventive Services Task Force han hecho recomendaciones en contra.

Objetivo

Estudiar la utilización de las determinaciones de TSH por los médicos de atención primaria, definir la población que tiene un mayor riesgo de presentar valores anormales de TSH a los 5 años y determinar el curso de la enfermedad tiroidea en las personas con valores iniciales anormales de TSH.

Perfil del estudio

Tipo de estudio: Estudio de cohortes
Área del estudio: Diagnóstico
Ámbito del estudio: Comunitario

Métodos

Se revisaron los registros informatizados de una aseguradora de Tel Aviv que atiende a más de 3 millones de personas para localizar las personas a las que se les había solicitado una determinación de TSH el año 2002. Se excluyó a las que tenían una determinación de TSH anormal o una enfermedad tiroidea antes de 2002, así como a las que estaban tratadas con litio, amiodarona o interferón en los 5 años anteriores y las que tuvieron un hijo durante el periodo de estudio. Se consideró que un individuo había desarrollado una enfermedad tiroidea si se había detectado una TSH >10 mUI/mL o <0 a="" administrado="" alguna="" alterada.="" an="" antitiroideos="" con="" de="" del="" estas="" evoluci="" excluyeron="" hab="" hormonas="" la="" le="" lisis="" los="" medicaciones="" ml="" mui="" n="" natural="" o="" p="" pacientes="" que="" radiactivo.="" recibieron="" se="" tambi="" tiroideas="" tsh="" yodo="">

Resultados

El año 2002 se solcitó una determinación de TSH a 422.242 personas que no tenían antecedentes de enfermedad tiroidea (18% de la población cubierta por la aseguradora). El 66% eran mujeres. La probabilidad de que se le determinase la TSH aumentaba con la edad, de forma que un se les había hecho esta determinación al 25% de las mujeres >40 años. El 95% de las determinaciones fueron normales, 1,2% estaban disminuidas, un 3% moderadamente elevadas y un 0,7% muy elevadas (>10 mUI/mL).
En el 89% de los individuos con valores anormales de TSH se hizo una determinación de T4 libre. Un 30% de las personas con valores de TSH muy elevados tenían valores de T4 libre por debajo de lo normal (hipotiroidismo franco). En un 93% de las que tenían TSH moderadamente elevadas las hormonas periféricas eran normales (hipotiroidismo subclínico). En un 9% de los que tenían valores de TSH bajos, la T4 estaba elevada (hipertiroidismo franco).
Se inició un tratamiento con hormonas tiroideas o antitiroideos en el 3,5% de los individuos. De los restantes, un 85% tenían nuevas determinaciones de la TSH (media 3,7 determinaciones por persona). La media de tiempo transcurrido entre la primera y las siguientes determinaciones fue de 19 meses. En el 98% de los individuos en los que la primera determinación fue normal, las sucesivas también lo fueron. Por otro lado, más de la mitad de las personas en las que los valores eran bajos o moderadamente altos, las determinaciones sucesivas fueron normales (fig. 1).

Figura 1. Resultados de las determinaciones sucesivas de TSH en función de los resultados iniciales.

Conclusiones

Los autores concluyen que la probabilidad de que la determinación de la TSH sea anormal después de una primera determinación normal es baja (2%) y que más de la mitad de los que han presentado valores anormales se normalizan en las determinaciones sucesivas.

Conflictos de interés

Ninguno declarado.

Comentario

Las disfunciones tiroideas francas son enfermedades crónicas que tienen consecuencias claras sobre la salud y que requieren tratamiento de por vida. La situación dista de estar tan clara en el caso de las disfunciones subclínicas, en las que las hormonas periféricas son normales y la TSH, no. Algunas sociedades científicas como la American Thyroid Association recomiendan un cribado cada 5 años en la población general a partir de los 35 años de edad, mientras que otras como la US Preventive Services Task Force, no.
Estas entidades son frecuentes en la población (se calcula que un 5% de las mujeres y un 3% de los varones las presentan), se dispone de una prueba relativamente accesible para su detección y de tratamientos para corregirlos. La principal duda reside en las consecuencias para la salud de estas situaciones y en la eficacia en términos de salud del tratamiento de estas condiciones.
En este trabajo, además se constata la imprecisión de la medida de la TSH, puesto que los valores anormales no tienden a mantenerse en el tiempo. En más de la mitad de las personas que presentaban un nivel de TSH anormal, los valores se normalizaban en los siguientes controles. Por otro lado, la tasa de progresión desde el hipotiroidismo subclínico al hipotiroidismo franco fue muy baja, aunque el periodo de seguimiento fue muy corto.

Bibliografía

1. Helfand M. Screening for subclinical thyroid dysfunction in nonpregnant adults: a summary of the evidence for the US Preventive Services Task Force. Ann Intern Med 2004; 140: 128-141.  R   TC   PDF
2. Surks MI, Ortiz E, Daniels GH, Sawin CT, Col NF, Cobin RH et al. Subclinical thyroid disease: scientific review and guidelines for diagnosis and management. JAMA 2004; 291: 228-238.  R   TC (s)   PDF (s)
3. Ladenson PW, Singer PA, Ain KB, Bagchi N, Bigos ST, Levy EG et al. American thyroid association guidelines for detection of thyroid dysfunction. JAMA Intern Med 2000; 160: 1573-1575.  R   TC (s)   PDF (s)

Autor

Manuel Iglesias Rodal. Correo electrónico: mrodal@menta.net.

Judgements about the quality of evidence

Judgements about the quality of evidence

A short glossary is included at the bottom of this page. For a more complete glossary click here: Glossary of terms.

In making health care treatment and delivery decisions, policymakers, patients and clinicians must trade off the benefits and downsides of alternative strategies. Decision-makers will be influenced not only by the best estimates of the expected advantages and disadvantages, but also by their confidence in these estimates; i.e. the quality of the evidence. The GRADE system, which we have used, provides a structured and transparent system for making judgements about the quality of evidence.¹

Using the GRADE system, we have made separate ratings of evidence quality for each important outcome. Like early systems of grading the quality of evidence, the GRADE system begins with the study design. Randomised trials provide, in general, stronger evidence than observational studies. Therefore, randomised trials without important limitations constitute high quality evidence. Observational studies without special strengths generally provide low quality evidence. However, there are a number of factors that can reduce or increase our confidence in estimates of effect.

The GRADE system considers five factors that can lower the quality of the evidence:

1. Study limitations
2. Inconsistent results across studies
3. Indirectness of the evidence
4. Imprecision
5. Publication bias

and three that can increase the quality of evidence:

1. Large estimates of treatment effect
2. A dose-response gradient
3. Plausible confounding that would increase confidence in an estimate

Factors that can lower the quality of evidence

1. Our confidence in estimates of effects decreases if studies suffer from major limitations that may bias their estimates of the treatment effect. These limitations include, for example, lack of allocation concealment; lack of blinding, particularly if outcomes are subjective and their assessment highly susceptible to bias; a large loss to follow-up; failure to adhere to an intention-to-treat analysis; or failure to report outcomes (typically those for which no effect was observed).
2. Widely differing estimates of effects across studies for which there are no compelling explanations reduces our confidence in knowing what the true effect is. Variability may arise from differences in populations (e.g., drugs may have larger relative effects in sicker populations); from differences in interventions (e.g., larger effects with higher drug doses); or outcomes (e.g., diminishing treatment effect with time). When variability exists but investigators fail to identify a plausible explanation, the quality of evidence decreases.
3. Decision-makers must consider two types of indirectness that can lower the quality of evidence. The first occurs when considering, for instance, use of one of two active drugs, A and B. Although randomized comparisons of A and B may be unavailable, randomized trials may have compared A to placebo and B to placebo. Such trials allow indirect comparisons of the magnitude of effect of A and B. Such evidence is of lower quality than head-to-head comparisons of A and B would provide. The second type of indirectness includes differences between the population, intervention, comparator to the intervention, and outcome of interest, and those included in the relevant studies. Most important here is consideration of whether important outcomes are measured directly or surrogate outcomes are used, such as a biochemical or process measure that may or may not accurately reflect what can be expected in terms of an important outcome, such as mortality or morbidity.
4. When studies include relatively few patients and few events and thus have wide confidence intervals (or a large p-value), we are less confident in an estimate.
5. The quality of evidence will be reduced if there is a high likelihood that some studies have not been reported (typically those that show no effect). The risk of such a “publication bias” is greater when published evidence is limited to a small number of trials, all of which are sponsored by people with a vested interest in the results, such as the pharmaceutical industry.

Factors that can increase the quality of evidence

1. Even well-done observational studies generally yield only low-quality evidence, because of the many potential confounders that either are not known or are not measured. However, occasionally they may provide moderate or even high quality evidence. The larger the magnitude of effect, the less likely it is that this could be explained by confounders and the evidence is, thus, stronger. For example, a meta-analysis of observational studies found that bicycle helmets reduce the odds of head injuries in cyclists involved in a crash by about two-thirds, an effect that could not easily be explained by confounders, given the design of the studies.
2. The presence of a dose-response gradient can increase our confidence in estimates of effects, for example if larger effects are associated with larger doses, as might be expected.
3. When an effect is found, if all plausible confounding would decrease the magnitude of effect, this increases the quality of the evidence, since we can be more confident that an effect is at least as large as the estimate and may be even larger. Conversely, particularly for questions of safety, if little or no effect is found and all plausible biases would lead towards overestimating an effect, we can be more confident that there is unlikely to be an important effect.
   GRADE provides a clearly articulated and comprehensive methodology for rating and summarising the quality of evidence supporting treatment and health care delivery recommendations. Although judgements will always be required for every step, the systematic and transparent GRADE approach allows scrutiny of and debate about those judgements.

[1] Guyatt GH, Oxman AD, Kunz R, Vist GE, Falck-Ytter Y, Schunemann HJ, and the GRADE Working Group. What is ‘quality of evidence’ and why is it important to clinicians? BMJ 2008; 336:995-8. 

Short glossary (for a more complete glossary click here: Glossary of terms)

Allocation concealment
The process used to ensure that the person deciding to enter a participant into a randomised controlled trial does
not know the comparison group into which that individual will be allocated.

Blinding
The process of preventing those involved in a trial from knowing to which comparison group a particular participant
belongs.

Bias
A systematic error or deviation in results or inferences from the truth.

Confidence interval
A measure of the uncertainty around the main finding of a statistical analysis.

Confounder
A factor that is associated with both an intervention (or exposure) and the outcome of interest. For example, if people in the experimental group of a controlled trial are younger than those in the control group, it will be difficult to decide whether a lower risk of death in one group is due to the intervention or the difference in ages. Age is then said to be a confounder, or a confounding variable.  Randomisation is used to minimise imbalances in confounding variables between experimental and control groups. Confounding is a major concern in non-randomised studies.

Intention-to-treat
A strategy for analysing data from a randomised controlled trial. All participants are included in the arm to which they were allocated, whether or not they received (or completed) the intervention given to that arm.

Loss to follow-up
The loss of participants during the course of a study.

Observational study
A study in which the investigators do not seek to intervene and simply observe the course of events. Changes or differences in one characteristic (e.g. whether or not people received the intervention of interest) are studied in relation to changes or differences in other characteristic(s) (e.g. whether or not they died), without action by the investigator.

Randomised trial
An experiment in which two or more interventions, possibly including a control intervention or no intervention, are compared by being randomly allocated to participants. In most trials one intervention is assigned to each individual but sometimes assignment is to defined groups of individuals (for example, in a household) or interventions are assigned within individuals (for example, in different orders or to different parts of the body).

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Medical Errors of Diagnosis Harm More Than Treatment Mistakes

Via: Healthy Skepticism (Facebook Page)

Source: ourhealthcaresucks

CGI image of rod piercing Phineas Gage's skull taken from NINDS public domain page at http://www.ninds.nih.gov/health_and_medical/pubs/tbi.htm (Photo credit: Wikipedia)

Another report on medical errors in America’s healthcare system cautions that these early-stage medical errors account for more medical harm – both preventable deaths and disability – than treatment errors.

In this study, Johns Hopkins researchers reviewed 25 years of medical errors – reflected in medical malpractice payouts – and found that diagnostic errors accounted for more payouts than surgical mistakes or medication overdoses.

Medical errors of improper diagnoses – missed, delayed and wrong diagnoses – accounted for the single largest proportion of such payouts at over a third. And twice as many occurred on an outpatient basis as among hospital inpatients, although the latter were more often lethal (48.4% vs. 36.9%).

The majority were missed diagnoses rather than delayed or wrong diagnoses – and the numbers are vastly understated because they only include those that led to malpractice claims. Most medical errors never reach the point of a malpractice claim.

15-40% Misdiagnoses?

Medical errors is a subject I address in-depth in Our Healthcare Sucks, including the following excerpts:

An estimated 15% of medical diagnoses are in error, with autopsy results showing diagnostic error rates in select areas up to 40%.[1]…

According to an article in The New England Journal of Medicine: [2]

 “125 million (Americans live) with chronic illness, disability, or functional limitation….

“The typical Medicare beneficiary saw two primary care physicians and five specialists (a) year…

“Patients with several chronic conditions may visit up to 16 physicians in a year.”

In a mostly seamless delivery system that emphasized and rewarded coordination of care and avoidance of redundant tests and procedures, maybe it would be possible to maintain quality despite such fragmentation of care.

But the U.S. medical delivery system is anything but “seamless” and, even if it were, it would still require a level of professional precision that’s sorely missing. Physician diagnostic error rates have been estimated as low as 5% (still 1 in 20) and as high as 40% based on autopsy results, with 15% the likely average ballpark.

That’s a huge margin of error that underscores the need to take what your doctor tells you with a grain of salt, at least until you’re able to get it confirmed by another opinion or by imaging or lab test results.

A special Supplement in The American Journal of Medicine [3] addressed this issue with a comprehensive review of the medical literature “Concerning teaching, learning, reasoning and decision making as they relate to diagnostic error and overconfidence”.[4]

According to the authors of this review:

“Being confident even when in error is an inherent human trait, and physicians are no exception. When directly questioned, many clinicians find it inconceivable that their own error rate could be as high as the literature demonstrates…

“This reflects both overconfidence and complacency (emphasis added).”

An article on this report makes this dismal observation:

“Medical practitioners really do not use systems designed to aid their diagnostic decision making…physicians have underutilized decision-support systems and misdiagnosis rates remain high.”

Say It Ain’t So, Doc

Many patients don’t want to believe their doctors are capable of such high rates of medical misdiagnoses – any more than they want to believe our higher rates of medical errors than other developed countries. These are very intelligent, well-educated people, after all – generally more so than the patients they treat. How can such smart people make so many diagnostic errors?

The obvious answer lay in our fee-for-service payment system that penalizes doctors for spending the time required for thoughtful assessments and diagnoses. These “cognitive” services are reimbursed at far lower levels than procedures – whether needed or not.

Medicare and other payers are partly to blame for perpetuating this skewed payment system, as are those in the medical profession who choose to maximize their incomes at their patients’ expense. No one’s forcing them to submit to these skewed incentives, after all. Some don’t, so it’s not impossible – but they’re generally considered either saints or fools by their peers.

Anchoring On First Impressions

But there’s more to our excess medical errors than that. Dr. Jerome Groopman helps us understand how doctors’ training perpetuates mental shortcuts and crutches that contribute greatly to medical errors – and to diagnostic errors specifically. Here’s another excerpt from Our Healthcare Sucks on the subject:

Enter Dr. Jerome Groopman and his New York Times best seller, How Doctors Think, in which he lays out many of the sources of medical error, misdiagnosis, and misjudgment.

He helps us understand how such smart people can make so many mistakes by explaining that it’s not a function of intellect.

Instead, it’s a type of cognitive dysfunction in which many physicians – who are trained and required by their business mandates to make snap judgments – can fall prey to all sorts of errors in thinking.

Here are a few sentences from Dr. Groopman’s book that bear on this discussion:

“Misdiagnosis is different (from medical errors)…experts studying misguided care have concluded the majority of errors are due to flaws in physician thinking, not technical mistakes…

“In one study of misdiagnoses…some 80% could be accounted for by a cascade of cognitive errors…ignoring information that contradicted a fixed notion….

“As many as 15% of all diagnoses are inaccurate…

“Physicians tend to go with their first impression…

“The cognitive mistakes that account for most misdiagnoses…largely reside below the level of conscious thinking (emphasis added).”[5]

Doctors, he explains, tend to get stuck on first impressions, something he calls “anchoring” because it anchors or fixates their diagnosis and often that of other doctors to whom you might be referred.

Or they just might not like certain patients, leading them to cut them off from fully describing their symptoms and settle for the most convenient or available treatment.

If they think the patient is a complainer or hypochondriac, they may assume a benign condition and minimize the likelihood of serious disease.

Now these cognitive flaws, as Dr. Groopman describes them, are all understandable as human failings – and Groopman quite understands them having labored with them himself – but are they professional?

Don’t patients have a right to expect more of their highly-paid doctors?

Why should patients have to worry about whether their doctor likes them or not and fear, quite correctly it seems, that it will bias their treatment?

This isn’t high school, after all.

Defensive Medicine is A Failed Response 

The medical profession has failed to address this crisis in patient safety with the urgency it deserves – opting instead for defensive medicine practices intended to insulate them from malpractice liability rather than address medical errors head-on.

This defensive mindset prevents the profession from engaging more meaningfully to correct both diagnostic and treatment failures.

And with more patients seeking medical care as Obamacare is implemented, doctors will have even less time to spend with patients. The risk of diagnostic errors – and medical errors generally – is likely to increase as a result.

A thorough diagnostic work-up takes time and thought – both of which are in increasingly short supply in America’s broken healthcare system.

All of which means patients will have to learn how to protect themselves and their loved ones when engaging with our fundamentally flawed medical system.

[1] The Autopsy as an Outcome and Performance Measure. Agency for Healthcare Research and Quality. Evidence Report/Technology Assessment. Number 58. Oct. 2002.

[2] Coordinating Care – A Perilous Journey through the Health Care System, The New England Journal of Medicine, Vol. 358:1064-1071, 3/6/08.

[3] The American Journal of Medicine, Volume 121, Issue 5A, May, 2008.

[4] Elsevier Health Sciences (2008, April 29). Will You Be Misdiagnosed? How Diagnostic Errors Happen. ScienceDaily.
[5] How Doctors Think. Dr. Jerome Groopman, Houghton Miflin Company.2007.

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Diabetic Patients with Uncontrolled Blood Pressure

Full Text 

Eve A. Kerr, MD, MPH; Brian J. Zikmund-Fisher, PhD; Mandi L. Klamerus, MPH; Usha Subramanian, MD, MS; Mary M. Hogan, PhD, RN; and Timothy P. Hofer, MD, MS

[+] Article and Author Information

Companion Article(s):

• It's Time to Overcome Clinical Inertia

Ann Intern Med. 2008;148(10):717-727. doi:10.7326/0003-4819-148-10-200805200-00004

Editors' Notes

Context

• Why do clinicians fail to intensify antihypertensive therapy when a patient's blood pressure is elevated?

Contribution

• This study involved 1169 diabetic patients seen by 92 primary care providers at 9 Veterans Affairs facilities. All had elevated triage blood pressures, but only half received antihypertensive treatment intensification by providers. Patient reports of home blood pressures or repeated blood pressures by providers within normal limits and discussion of medication issues decreased the likelihood of antihypertensive intensification at clinic visits.

Implication

• Uncertainty about true blood pressure values may underlie many reasons why physicians do not intensify antihypertensive therapy.

—The Editors

Despite some recent improvements in blood pressure control, the number of patients with inadequate control remains high and contributes to excess morbidity and mortality, especially among patients at high risk from complications of hypertension (1 - 8). Several studies have suggested that “clinical inertia”—the failure by providers to initiate or intensify therapy (medication intensification) in the face of apparent need to do so—is a main contributor to poor control of hypertension (9 - 12).

Although the failure to intensify treatment medications for patients with elevated blood pressures at visits has been well documented ((5 - 6), (12 - 18)), factors underlying what seems to be clinical inertia have been studied less systematically. When providers are queried after clinic visits about the lack of medication intensification for elevated blood pressure, they variously report that the patient's “true” blood pressure was lower than the clinic blood pressure reading, that other patient concerns precluded attention on blood pressure management, and that patient adherence should be improved before medication intensification ((6), (17)). Some studies have examined the role of various clinical and patient factors in intensification decisions ((6), (8), (17), (19 - 20)), but no study has used a detailed conceptual model to comprehensively examine the relative contribution of a broad array of potential patient, provider, organizational, and visit-specific contributors to a medication intensification decision. In addition, although a frequently cited reason for deferring medication changes is that the clinic blood pressure does not reflect the patient's “true” blood pressure (21 - 22), this clinical uncertainty and its effects have not been explored.

To better understand factors underlying apparent clinical inertia for hypertension, we designed the ABATe (Addressing Barriers to Treatment for Hypertension) study to examine treatment change decisions for diabetic primary care patients with elevated triage blood pressures before a primary care visit. We defined elevated blood pressure for this population to be 140/90 mm Hg, a value well above guideline targets for diabetic patients and one clearly requiring some type of action (4). Our goals were to assess how often patients presenting with an elevated triage blood pressure received medication intensification or were scheduled for close follow-up and the role that clinical uncertainty about blood pressure, competing demands and prioritization, medication-related factors, and care organization play in treatment change decisions.

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What does "evidence" mean in chronic pain

Source: Bandolier

 

Clinical bottom line

As well as well understood biases, new forms of bias or potential bias in chronic pain studies are emerging. Unless we take care, we can make the wrong decisions when comparing therapy efficacy unless like is compared with like, and at the highest level of evidence.

---

Moore et al. "Evidence" in chronic pain--establishing best practice in the reporting of systematic reviews. Pain 2010 150: 386-389.

Starting point

We know that there are limitations in evidence, and that higher quality studies produce, almost overwhelmingly, more conservative results than those with less rigorous methods. Understanding exactly what quality, or validity, means, is a dynamic, and we have to keep relearning lessons and ratcheting up the minimum standards we accept for "evidence". This review casts a cold and fishy eye over evidence in chronic pain trials.

Suggestions

The review makes the following points about what constitutes good evidence:

• Randomisation, preferably properly done and concealed.
• Blinding, preferably properly done so that all concerned are unaware of treatment.
• Imputation method. This is a new one, and recognises that withdrawal rates in chronic pain trials can be high - up to 50-60% in chronic low back pain. Right now, the last observation carried forward method is commonly used, so that patients not taking the medicine can contribute to efficacy estimates. The recommendation is that this not be done, and that only those patients taking the medicine contribute, as no analgesia can come if you don't take the medicine. Right now there is little evidence on this, but more is emerging, and this could have a major effect on efficacy estimates.
• Study duration should be reasonably long - ideally 12 weeks. There is increasing evidence from longitudinal individual patient analyses that short studies can overestimate treatment effects, particularly for less effective therapies.
• Average pain score data will mislead. Responder analyses should be used instead. Again, individual patient data analysis indicates that distributions in pain trials are U-shaped, not Gaussian, making average values rather silly.
• There may be exaggerated treatment effects in crossover trials. and a predominance of crossover trials may be considered a possible source of additional bias.
• Size is crucially important, and when there are fewer than 200 events (an event would be a patient achieving a given level of response, for example), then results cannot be trusted, simply because of the magnitude of random chance effects.

Core outcomes

It seems likely that future systematic reviews, and trial analysis, will be based on some core outcomes of benefit and harm. A likely set of outcomes for chronic pain is likely to include some or most of the following:

• Pain
• - At least 50% pain reduction
• - At least 30% pain reduction
• - Proportion below 30/100 mm (no worse than mild pain)
• - Patient global impression (very much improved)
• Function
• General
• - Quality of life measure
• - Patient global impression
• Adverse events
• - Withdrawal due to adverse event
• - Serious adverse events
• - Death

Making comparisons

Indirect comparisons between treatments can be made where there is an adequate amount of good quality data. Comparability is imperilled when like is not compared with like - when, for example, results from small, short studies with easily-attained but inadequate outcomes are compared with large, long duration studies with clinically relevant outcomes that are hard to attain. It is also imperilled when different doses or treatments are erroneously combined under a general label and then discussed as identical. Meaningful comparison of efficacy with other interventions is not possible where quality, validity, and size standards are not met by any one of the comparators.
Yet making comparisons between treatments is what we try to do all the time, in making decisions about individual patients, when making policy, and making guidelines. The bottom line is that we will have to be much more careful in future.

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