One (effect) size does not fit at all: Interpreting clinical significance and effect sizes in depression treatment trials
Bibliographic record
Abstract
The efficacy of antidepressants in major depressive disorder has been continually questioned, mainly on the basis of studies using the sum-score of the Hamilton Depression Rating Scale as a primary outcome parameter. On this measure antidepressants show a standardised mean difference of around 0.3, which some authors suggested is below the cut-off for clinical significance. Prompted by a recent review that, using this argument, concluded antidepressants should not be used for adults with major depressive disorder, we (a) review the evidence in support of the cut-off for clinical significance espoused in that article (a Hamilton Depression Rating Scale standardised mean difference of 0.875); (b) discuss the limitations of average Hamilton Depression Rating Scale sum-score differences between groups as measure of clinical significance; (c) explore alternative measures of clinical importance; and (d) suggest future directions to help overcome disagreements on how to define clinical significance. We conclude that (a) the proposed Hamilton Depression Rating Scale cut-off of 0.875 has no scientific basis and is likely misleading; (b) there is no agreed upon way of delineating clinically significant from clinically insignificant; (c) evidence suggests the Hamilton Depression Rating Scale sum-score underestimates antidepressant efficacy; and (d) future clinical trials should consider including measures directly reflective of functioning and wellbeing, in addition to measures focused on depression psychopathology.
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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 | 0.659 | 0.852 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.013 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".