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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".