Bibliographic record
Abstract
A systematic review of the use of triptans in acute migraine I have read with interest and learned a great deal from the systematic review of triptans in acute migraine by Gawel, Worthington and Maggisano. 1 However, I have some concern on the method used to derive the numbers needed to treat (NNT) from various estimates of therapeutic gains.Numbers needed to treat are often used to summarise treatment effects in a clinically relevant way.It is, however, widely believed that NNTs from meta-analyses of risk differences are not reliable, since an NNT is specific to a control group event rate (e.g., placebo response in triptan trials).Smeeth and colleagues reviewed the use of NNTs to summarize treatment effect of statins for lowering cholesterol concentration in the prevention of coronary heart disease.While all treatments show very similar reductions in relative risk (i.e., a robust outcome measure of treatment effect), the associated NNTs derived from risk difference vary up to two-fold depending on the clinical settings.They show that the pooled NNTs from risk differences can be misleading because the baseline risk often varies appreciably between trials and suggest that if NNTs are to be calculated, they should be based on relative measures, and presented for a variety of stated baseline risk. 2 The NNTs derived from the pooled therapeutic gains with triptans that were reported in the review may not display
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.027 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".