© 2004 Canadian Medical Association or its licensors Letters
Bibliographic record
Abstract
SSRI treatment for under-18s Let me see if I’ve got this straight. In-dustry bias in reporting the results of trials of selective serotonin reuptake inhibitors (SSRIs) is probable,1 although an employee of a multinational drug company in another country doubts that this is so.2 Another therapist (the author of a book reviewed in CMAJ) feels that mental illness has become far too com-mercialized (and the reviewer notes that, for pointing this out, the book’s author has been personally maligned).3 Data are lacking because they are not known (be-cause of a globally inadequate system for reporting adverse drug reactions4), are not available (because they have not been published5) or cannot be discussed by physicians who have engaged in nondisclosure contracts.6 SSRIs may be associated with an incidence of serious side effects (including withdrawals) of up to 25%, and the placebo response rate can be as high as 40 % to 60%, although there may be a 70 % response rate on some criteria of depression.6 There is no evidentiary basis to prescribe or not pre-scribe SSRIs in patients under 18 years of age, and, either way, all of these uses are “off label ” for patients in this age group. Furthermore, no clear leadership position is evident among child psychia-trists, almost all of whom are aligned with this controversy in some way. As a personal standard, I try never to complain without offering some con-structive suggestion. Having perused a selection of the currently available world literature on this topic, my im-pression is that SSRIs should be used with caution in this age group, and only as a last resort, after the failure of all other obvious psychosocial and envi-ronmental interventions, and with close attention to symptoms of mood insta-bility (serious adverse behavioural and emotional reactions including agitation, irritability, behavioural disinhibition and suicidality).
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.779 | 0.617 |
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".