Bibliographic record
Abstract
Can J Psychiatry. 2009;54(2):75. We live in a world where if treatments work and are relatively risk-free, health care providers, rather than using doctors, are increasingly using nurses and others, who in addition to being less costly to employ are more likely to adhere to guidelines. Against this background, when black box warnings were first put on antidepressants, the American Psychiatric Association (APA) issued a press release that was little short of a suicide note stating: the APA believes antidepressants save lives.1 Dr Brent's article concedes that antidepressants can cause a problem. What appears at issue is the scale of the problem. On the one hand, he gives us number-needed-to-treat figures of 3 for the benefit from treatment and 121 for the harm. However, numerous people receiving a benefit will in fact be harmed in the longer run by, among other things, physical dependence on treatment or sexual dysfunction that in some cases will persist for years after treatment stops. On the other hand, about 1 in 4 patients in trials show some initial increase in anxiety on treatment; while 1 in 20 drop out of trials owing to agitation or related problems, up to 1 in 4 children may show growth failure and 1 in 2 sexual dysfunction. Given figures like this, it is not clear that balancing a rating scale benefit against only one of many harms antidepressants can trigger offers guidance on when to treat. Two hundred years ago, Philippe Pinel framed the reason for having professionals involved in delivering medical care as follows: It is an art of no little importance to administer medicines properly, but it is an art of much greater and more difficult acquisition to know when to suspend or altogether to omit them.2, p 10 No dataset better supports such a philosophy than the data on antidepressants, which indicate that most recoveries on antidepressants would have happened whether or not the person was put on treatment. A further reason why treatments such as antidepressants are available from medical professionals only has been because politicians once thought that doctors would be able to quarry data out of companies in a way that patients would not. However, in the case of the antidepressants, it has been nonmedical people who have unearthed the data on hazards. Nonmedical people have revealed that the data in the infamous study 329 shows a much higher risk of suicidality than has found its way into the dataseis Dr Brent depends on. …
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.063 | 0.066 |
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".