Across-sectional study of prescribing patterns in chronic psychiatric patients living in sheltered housing facilities
Bibliographic record
Abstract
Objective: To analyze prescribing patterns of chronic psychiatric patients living in sheltered housing facilities, to identify the extent of polypharmacy and to estimate associated risks in this patient group. Methods: In a retrospective cross-sectional study the prescription data of 323 chronic psychiatric patients (average age 48.5 years) living in sheltered housing facilities in Rotterdam, The Netherlands, were analyzed. Prescription data were obtained from pharmacy-dispensing records. Results: Patients received on average 4.6 drugs (95% CI, 4.3-4.9). The most frequently prescribed drugs were as expected antipsychotics, benzodiazepines and antimuscarinic drugs. Overall 25% (n = 81) of patients received two or more antipsychotic drugs. A high proportion of patients (38%, n = 124) received one benzodiazepine, and 15% (n = 50) received two or more benzodiazepines. Conclusion: Patients in our study received a worryingly high number of drugs, and a quarter of the population was subject to antipsychotic polypharmacy. This increases the risk that drug-drug interactions, adverse drug reactions and noncompliance occur. Our study indicates potentially low quality of prescribing and shows the need for reviewing and special monitoring of pharmacotherapy in this patient group. © 2008 Dustri-Verlag Dr. K. Feistle.
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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 | 0.003 |
| 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) | 0.002 | 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".