Determining the optimal measurement for general practitioner encounters following stroke using linked data from the Australian Stroke Clinical Registry
Bibliographic record
Abstract
Background General practitioners (GPs) provide ongoing support after a stroke, but little is known about these encounters and the metrics to measure them. Objective To compare methods for measuring patterns of GP encounters following stroke in survival models. Methods We performed a landmark analysis using data from the Australian Stroke Clinical Registry (2010–2014) linked with Australian Medicare claims (2009–2016) to determine GP encounters within 18 months following stroke. Continuity of GP encounters (consistency) and regularity (distribution) were each calculated using 3 indices. Indices were compared based on 1-year survival using multivariable Cox regression models. The best performing measures of regularity and continuity, based on model fit, were combined into a composite ‘optimal care’ variable. Results Among 10,728 registrants (43% female, 69% aged ≥65 years), the median number of encounters was 17 (Q1: 10, Q3: 26) within 18 months. The measures most strongly associated with survival (hazard ratio [95% confidence interval], Akaike information criterion [AIC], Bayesian information criterion [BIC]) were the Continuity of Care Index (COCI, as a measure of continuity; 0.88 [0.76–1.02], p = 0.099, AIC = 13746, BIC = 13855) and our persistence measure of regularity (encounter at least every 6 months; 0.80 [0.67–0.95], p = 0.011, AIC = 13742, BIC = 13852). Our composite measure, persistence plus COCI ≥80% (0.80 [0.68–0.94], p = 0.008, AIC = 13742, BIC = 13851), performed marginally better than our persistence measure alone. Conclusion GP continuity and regularity of care are important indicators of ongoing support after stroke. Our persistence measure of regularity or composite indice may be useful measures of patient outcome with respect to general practitioner encounters following stroke.
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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 | 0.029 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".