Primary care physician volume and quality of care for older adults with dementia: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Some jurisdictions restrict primary care physicians' daily patient volume to safeguard quality of care for complex patients. Our objective was to determine whether people with dementia receive lower-quality care if their primary care physician sees many patients daily. METHODS: Population-based retrospective cohort study using health administrative data from 100,256 community-living adults with dementia aged 66 years or older, and the 8,368 primary care physicians who cared for them in Ontario, Canada. Multivariable Poisson GEE regression models tested whether physicians' daily patient volume was associated with the adjusted likelihood of people with dementia receiving vaccinations, prescriptions for cholinesterase inhibitors, benzodiazepines, and antipsychotics from their primary care physician. RESULTS: People with dementia whose primary care physicians saw ≥ 30 patients daily were 32% (95% CI: 23% to 41%, p < 0.0001) and 25% (95% CI: 17% to 33%, p < 0.0001) more likely to be prescribed benzodiazepines and antipsychotic medications, respectively, than patients of primary care physicians who saw < 20 patients daily. Patients were 3% (95% CI: 0.4% to 6%, p = 0.02) less likely to receive influenza vaccination and 8% (95% CI: 4% to 13%, p = 0.0001) more likely to be prescribed cholinesterase inhibitors if their primary care physician saw ≥ 30 versus < 20 patients daily. CONCLUSIONS: People with dementia were more likely to receive both potentially harmful and potentially beneficial medications, and slightly less likely to be vaccinated by high-volume primary care physicians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".