The usefulness of large studies in psychopharmacology: understanding their strong points and their drawbacks
Bibliographic record
Abstract
Large studies have recently been reported in relation to the use of antidepressant and antipsychotic medications. They were designed to assess, in a controlled manner, the effectiveness or safety (or both) of such medications. These include the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Study on the use of antipsychotic drugs in schizophrenia, 1‐3 the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) studies on multiple steps of antidepressant treatments, 4‐10 and the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) program in bipolar illness. 11‐13 These endeavours addressed crucial issues concerning the use of psychopharmacological agents, and the results obtained are precious to the field. Certain problems, however, may stem from the interpretation of the data associated with such large bodies of work, either by the authors or by parties that can benefit from focusing on a single aspect of such studies. The following results have received the most attention from the CATIE trials: the observation that the atypical antipsychotics did not appear to provide greater effectiveness when compared with the typical antipsychotic perphenazine and that perphenazine was devoid of negative metabolic impact.
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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 | 0.123 | 0.248 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.017 | 0.033 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".