I Have the Answer, Now What's the Question?: Why Metaanalyses Do Not Provide Definitive Solutions
Bibliographic record
Abstract
It ain't so much the things we don't know that get us in trouble.It's the things we know that just ain't so.-Artemus Ward I n a recent issue of the British Medical Journal, Moncrieff and Kirsch (1) concluded that "Recent meta-analyses show selective serotonin reuptake inhibitors [SSRIs] have no clinically meaningful advantage over placebo" and that "Methodological artefacts may account for the small degree of superiority shown over placebo."Needless to say, this article generated a large number of letters to the editor, citing everything from despair about the lack of available alternatives to charges that the authors overlooked or ignored evidence about the positive effects of SSRIs, that they misinterpreted the findings and recommendations of the National Institute for Health and Clinical Excellence (NICE), that both the authors and NICE used inappropriate criteria to evaluate improvement, that the authors made erroneous assumptions about the distribution of depression, and so on.This editorial does not aim to critique the article by Moncrieff and Kirsch.Rather, it tries to explain why different people with honourable intentions can come to different conclusions regarding metaanalyses.Suffice it to say for now that their summary and recommendations are by no means accepted by all.Other metaanalyses (for example, 2-4), including one coauthored by Moncrieff (5), have supported the use of SSRIs; this article has merely brought the controversy to a head.
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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 | 0.275 | 0.595 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.035 | 0.050 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".