Prescribing patterns for Alzheimer disease: survey of Canadian family physicians.
Bibliographic record
Abstract
OBJECTIVE: To describe Canadian family physicians' prescribing practices with regard to Alzheimer disease (AD). DESIGN: Cross-sectional survey administered by facsimile. SETTING: Four regions in Canada (British Columbia, the Prairie Provinces, Ontario, and the Atlantic Provinces). PARTICIPANTS: A stratified random sample of 1000 Canadian family physicians (250 per region) chosen from the Canadian Medical Directory; 81 of whom were excluded as ineligible. MAIN OUTCOME MEASURES: Prescribing practices regarding cholinesterase inhibitors (ChIs) for patients with AD. RESULTS: Response rate was 36.3%. About 27% of respondents reported that ChIs were prescribed for less than 10% of their AD patients, while 12.5% reported that ChIs were prescribed for more than 90% of their AD patients. More physicians prescribed ChIs in the two regions with provincial formulary coverage (Prairie Provinces and Ontario) than in the two regions without coverage (British Columbia and Atlantic Provinces). Factors that significantly predicted lower prescribing rates included female sex, perception of ChIs' effectiveness, and self-reported knowledge of ChIs. CONCLUSION: Canadian physicians' prescribing patterns for ChIs vary; the optimal prescribing rate is unclear. Provincial coverage of these drugs along with physicians' sex, knowledge of ChIs, and perception of the effectiveness of ChIs appear to influence prescribing rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".