Expressing the magnitude of adverse effects in case-control studies: "the number of patients needed to be treated for one additional patient to be harmed"
Bibliographic record
Abstract
The need to express estimates of risk in an understandable manner is a challenge faced regularly by those who work with the results of epidemiological studies and try to convey their meaning to others. This is not an easy task, as is illustrated by the recent “pill scare” in the United Kingdom, in which there was much confusion over the clinical importance of the scientific information that was made public. Furthermore, practising clinicians also need a readily understandable tool for weighing the risks of various treatments. Ideally, this should be feasible without recourse to complicated statistical concepts. In this paper, we propose a simple and intuitively understandable method for expressing the results of case-control studies. #### Summary points Results of epidemiological studies need to be expressed in understandable terms if they are to be of practical use to clinicians and policy makers Case-control studies are often used to study adverse effects of treatment; odds ratios from these are used to express the magnitude of adverse effects, but are not intuitively understandable estimates of risk A more understandable and informative means of expressing the risk of adverse events in case-control studies is “the number of patients needed to be treated for one additional patient to be harmed” This is calculated from the odds ratio and the unexposed event ratemdash;that is, the rate of occurrence of the adverse event of interest in people not exposed to the treatment Any intervention or exposure may have desirable and undesirable effects. Desirable effects are usually the intended effects of a treatment. These will often (at least for pharmacological interventions) have been established in randomised controlled trials before an agent is released onto the market and introduced into clinical practice. In the context of randomised trials on the desirable effects of treatments, Sackett et al proposed a method for …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.305 | 0.462 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".