Reply: Atypical Antipsychotic Use in Treating Adolescents and Young Adults with Developmental Disabilities
Bibliographic record
Abstract
Friedlander and others note that, even with their clinic’s conservative prescription practices, one-half the individuals in the sample were taking atypical antipsychotics—even when psychotic symptoms were not documented. We agree with the authors that, in the absence of clearly identified psychiatric disorders for which these medications are indicated, the practice of using either typical or atypical antipsychotics to treat behaviour disturbances is no longer tenable. We urge psychiatrists to identify and carefully monitor, in both their research and clinical practice, the target symptoms that the antipsychotic is intended to address, particularly when the symptoms are not psychotic (as may have been the case for many o f t he i ndividuals w ith autism–PDD in the present sample). Proceeding in this way helps to ensure that the old practice of overprescribing neuroleptics, noted by Friedlander and others, does not transfer into overprescribing newer antipsychotic medications, particularly in situations where the prescribing physician does not have access to a comprehensive multidisciplinary evaluation process.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".