The Quality of Antipsychotic Drug Prescribing in Nursing Homes
Bibliographic record
Abstract
BACKGROUND: The prescribing of antipsychotic drugs has been increasing in nursing homes (NHs) since the availability of second-generation antipsychotic agents, also known as the atypicals, but there is little information on the appropriateness of such prescribing. METHODS: A retrospective analysis using the nationally representative data set of the Medicare Current Beneficiary Survey merged to Minimum Data Sets assessments, medication administration records, and Medicare claims. We identified a sample of 2.5 million Medicare beneficiaries in NHs during 2000-2001 (unweighted n = 1096) to assess prevalence of antipsychotic use, rates of adherence to NH prescribing guidelines, and changes in behavioral symptoms. RESULTS: Approximately 693 000 (unweighted n = 302), or 27.6%, of all Medicare beneficiaries in NHs received at least 1 prescription for antipsychotics during the study period: 20.3% received atypicals only; 3.7%, conventionals only; and 3.6%, both atypicals and conventionals. Less than half (41.8%) of treated residents received antipsychotic therapy in accordance with NH prescribing guidelines. One (23.4%) in 4 patients had no appropriate indication, 17.2% had daily doses exceeding recommended levels, and 17.6% had both inappropriate indications and high dosing. Patients receiving antipsychotic therapy within guidelines were no more likely to achieve stability or improvement in behavioral symptoms than were those taking antipsychotics outside the guidelines. CONCLUSIONS: This study detected the highest level of antipsychotic use in NHs in over a decade. Most atypicals were prescribed outside the prescribing guidelines and for doses and indications without strong clinical evidence. Failure to detect positive relationships between behavioral symptoms and antipsychotic therapy raises questions about the appropriateness of prescribing.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".