PRACTICE DIFFERENCES BETWEEN THE UNITED STATES AND CANADA
Bibliographic record
Abstract
To the Editor: Bronskill et al., in their study of a large Ontario nursing home population between April 1, 1998, and March 31, 1999, estimated a substantial incident use of neuroleptics (17%) dispensed for behavioral disorders to older adults newly admitted to nursing homes.1 They also documented more prevalent use of conventional agents than of atypical agents and the adoption of doses higher than those recommended by the U.S. Medicare State Operations Manual. A completely different picture emerges from another study on the pattern and correlates of neuroleptic use by residents of U.S. nursing homes.2 This study analyzed data from long-stay residents (living in the facility for at least 1 year) in five states (Ohio, South Dakota, Maine, Mississippi, Kansas) between January 1, 1999, and January 31, 2000. Overall, the pattern of neuroleptic use appeared to be indicative of good practice. In fact, the prescription of neuroleptics was restricted to patients with schizophrenia or other organic psychoses and to patients with cognitive impairment associated with behavioral symptoms. Among the latter (n=86,514), the prevalence of neuroleptic use was 18.2%. Atypical neuroleptics appeared to be the most widely prescribed medications (approximately 63%, vs 37% of conventional agents). This correlates with further evidence from the Systematic Assessment of Geriatric Drug Use via Epidemiology database, which shows that, over the last few years, newer atypical neuroleptics have progressively replaced conventional agents (see Figure 1 with data from Ohio between 1998 and 2000) despite unchanged overall prevalence of use. Such changes in nursing homes follow previous evidence suggesting that, in the United States, newer atypical neuroleptics have been increasingly adopted in several medical settings.3 Furthermore, we documented that the dosages for all neuroleptics appear to be in accordance with Food and Drug Administration recommendations and are lower than the recommended daily threshold according to the U.S. Medicare State Operations Manual. The differences observed between Canadian and U.S. nursing homes may be largely attributable to the effect of policy on prescribing practice in the United States. Before the 1990s, the use of neuroleptics in U.S. nursing homes was widespread and poorly regulated.4 The Omnibus Budget Reconciliation Act of 1987 guidelines, which are currently in effect, regulate the use of neuroleptics in nursing homes, restricting their prescription to patients who present definite diagnostic indication and provide specific standards for allowable dosages for individual drugs.5 Although atypical agents have been recommended as appropriate first-line pharmacological treatment for behavioral and psychotic symptoms in nursing home residents,6 doubts about their safety have been cast. Recently, the Food and Drug Administration has warned U.S. physicians about a possible cerebrovascular risk associated with risperidone and olanzapine in elderly patients with dementia.7,8 A similar warning by the manufacturer of risperidone directed to Canadian physicians was issued nearly 2 years ago.9 It would be of interest to continue to register the changes in prescribing patterns of neuroleptics in U.S. and Canadian nursing homes. Temporal trend (1998–2000) in the pattern of neuroleptic use of Ohio nursing home residents (mean number of residents per year 120,105).
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".