Reservations about study on antidepressant use by young people and suicidal behaviour after FDA warnings
Bibliographic record
Abstract
We agree with several of Lu and colleagues’ conclusions.1 However, we have serious reservations about their conclusion that antidepressant warnings discouraged appropriate pharmacotherapy for depression, resulting in more suicidal behaviours by patients with unmedicated depression. Firstly, we agree that there was no appreciable increase in completed suicide rates in young people coinciding with the warnings, as confirmed by Barber and colleagues.2 Indeed, in 2007, after the warnings, the US adolescent suicide rate reached a 25 year low.3 The quarterly suicide rates in adults presented by Lu and colleagues are generally below age specific US suicide rates, which suggests under-reporting. However, we would not expect systematic under-reporting of suicides to affect the trend analyses if the level of under-reporting remained constant. We also agree that a trend change in antidepressant usage occurred after the warnings. However, it is unclear whether this change is entirely due to the warnings because promotional spending on antidepressants by drug companies dropped by about a third during the same period (by roughly $200m (£124m; €158m) per quarter between 2004 and 2006.4 We agree, too, that psychotropic …
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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 | 0.020 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.034 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".