Bibliographic record
Abstract
BACKGROUND: Antipsychotics are effective in treating the symptoms of schizophrenia, but they may induce adverse effects, some of which-those that impact negatively on physical appearance-have not been sufficiently discussed in the psychiatric literature. AIM: Through a narrative review, to catalog antipsychotic side effects that interfere with physical attractiveness and to suggest ways of addressing them. METHOD: PubMed databases were searched for information on the association between "antipsychotic side effects" and "attractiveness" using those two search phrases plus the following terms: "weight," "teeth," "skin," "hair," "eyes," "gait," "voice," "odor." Data from relevant qualitative and quantitative articles were considered, contextualized, and summarized. RESULTS: Antipsychotics, as a group, increase weight and may lead to dry mouth and bad breath, cataracts, hirsutism, acne, and voice changes; they may disturb symmetry of gait and heighten the risk for tics and spasms and incontinence, potentially undermining a person's attractiveness. CONCLUSIONS: Clinicians need to be aware of the impact of therapeutic drugs on appearance and how important this issue is to patients. Early in treatment, they need to plan preventive and therapeutic strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".