Irving Kirsch opens a window on antidepressant medications
Bibliographic record
Abstract
When it comes to antidepressant medications - popular, backbone drugs of modern psychiatry - even learned scholars and savvy clinicians find it difficult to separate honest, rigorous research from that which thrives on hidden agendas and ulterior motives. Fortunately, a mounting corpus of data-based studies, mostly meta-analyses, casts new and critical light on the clinical efficacy, side effects, and therapeutic outcomes of antidepressants. Spearheading these efforts over the past few decades, Irving Kirsch and colleagues have challenged the hegemonic view of antidepressants as an effective therapeutic intervention. Notably, Kirsch illuminates the small difference between antidepressants and placebos in mitigating depression-a difference that may be statistically significant yet fails to reach clinical significance. This piece sketches the important contributions Kirsch has made to the scientific understanding of antidepressant medications.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".