P632Relationship of outpatient provider volume and lipid screening performance measure adherence among patients at risk of cardiovascular disease
Bibliographic record
Abstract
Background: In-hospital myocardial infarction and heart failure patient volumes are associated with improved cardiovascular (CV) outcomes. Whether outpatient primary care physician (PCP) volumes are predictive of CV preventive care is unknown. Methods: In the multicenter, big data CANHEART observational study of the population of Ontario, Canada, patients 40–74 years without CV disease were evaluated for lipid screening between 2008 and 2012 with de-identified data using linked administrative healthcare databases according to their PCP's outpatient clinical volume of concordant patients. Patient volume was defined as the mean annual number of clinic visits made to the usual care PCP. Modified Poisson regression models were used to derive the relative risk (RR) of lipid screening according to patient volumes (measured continuously and according to quintiles) with adjustment for individual clinical and sociodemographic risk factors and physician characteristics. Results: There were 4,753,994 patients seen by 10,307 usual care PCPs during the study period. Overall, 83.8% of patients underwent lipid screening at least once over the 5-year period as recommended by regional guidelines. After multivariable adjustment, there was a stepwise increase in the RR across each quintile of patient volume (Figure), with a 3.3% (95% CI, 3.2–3.5) higher lipid-screening rate for every doubling of patient volumes (P<0.001).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".