Conventional, Atypical, and Combination Antipsychotic Prescriptions
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine if there is a relationship between the type of antipsychotic prescribed (conventional, atypical, or a combination) and patients' use of psychiatric services and prescription of adjuvant medications. METHOD: A chart review of 83 outpatients with long-term psychiatric disorders recorded the type and dosage of psychiatric medications prescribed in 1997-1998 (T1) and 2 years later, in 1999-2000 (T2). Psychiatric service use was also noted during the 2-year follow-up. RESULTS: Atypical prescriptions increased from 27% (N = 22) to 45% (N = 37) 2 years later. At T2, 35% of patients (N = 29) were prescribed conventionals, and 19% (N = 16) were prescribed a combination of conventionals and atypicals. The mean antipsychotic dosage in chlorpromazine equivalents (546.5 mg/day) increased significantly (p <.05). There was no difference between the 3 groups in their use of psychiatric services or the prescription of adjuvant medications, with the exception of less common prescription of anticholinergics. There was also no difference in psychiatric service use between patients who remained on treatment with combined antipsychotics at T1 and T2 (11%; N = 9) and the rest of the sample. Patients who were switched from one type of antipsychotic to another made more use of psychiatric services, however. CONCLUSION: Contrary to our expectations, patients prescribed combined antipsychotic types did not make more use of psychiatric services or use more adjuvant medications. The high percentage of patients prescribed a combination may be due to antipsychotic polypharmacy preferences and may represent a very slow crossover from one antipsychotic to another.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".