The role of meta-analyses and umbrella reviews in assessing the harms of psychotropic medications: beyond qualitative synthesis
Bibliographic record
Abstract
ὠφελέειν, ἢ μὴ βλάπτειν (Primum non nocere) - Hιppocrates' principle should still guide daily medical prescribing. Therefore, assessing evidence of psychopharmacologic agents' safety and harms is essential. Randomised controlled trials (RCTs) and observational studies may provide complementary information about harms of psychopharmacologic medications from both experimental and real-world settings. It is considered that RCTs provide a better control of confounding variables, while observational studies provide evidence from larger samples, longer follow-ups, in more representative samples, which may be more reflective of real-life clinical scenarios. However, this may not always hold true. Moreover, in observational studies, safety data are poorly or inconsistently reported, precluding reliable quantitative synthesis in meta-analyses. Beyond individual studies, meta-analyses, which represent the highest level of 'evidence', can be misleading, redundant and of low methodological quality. Overlapping meta-analyses sometimes even reach different conclusions on the same topic. Meta-analyses should be assessed systematically. Descriptive reviews of reviews can be poorly informative. Conversely, 'umbrella reviews' can use a quantitative approach to grade evidence. In this editorial, we present the main factors involved in the assessment of psychopharmacologic agents' harms from individual studies, meta-analyses and umbrella reviews. Study design features, sample size, number of the events of interest, summary effect sizes, p-values, heterogeneity, 95% prediction intervals, confounding factor adjustment and tests of bias (e.g., small-study effects and excess significance) can be combined with other assessment tools, such as AMSTAR and GRADE to create a framework for assessing the credibility of evidence.
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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 | 0.335 | 0.650 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.028 | 0.018 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".