GP claims for completing diabetes 'cycle of care'.
Bibliographic record
Abstract
BACKGROUND: There is a wide variability in the level of claims for diabetes. Service Incentive Payments (SIP) by general practitioners. METHOD: Cross sectional comparison of the ratio of the number of SIP items claimed between August 2002 and July 2003 to the estimated prevalence of diabetes by divisions of general practice (DGP). PARTICIPANTS AND SETTING: Seventy-nine of the 101 DGP with diabetes programs in 2002. RESULTS: The average ratio of diabetes SIP claims to estimated diabetes prevalence (including both diagnosed and undiagnosed cases) for each quarter of the year between August 2002 and July 2003 was 10.1% (standard deviation 3.6). This ratio was higher in DGP with a more disadvantaged population, and more of their GP members in large practices. The provision of IT support in DGP and the proportion of GPs who had patients registered on the division's register were associated with a higher ratio of claims. A multiple regression model with two factors: socioeconomic disadvantage and the proportion of GP members in practices of five or more GPs predicted 41% of the variance. CONCLUSION: Divisions of general practice appear to be supporting practices serving disadvantaged populations to deliver quality care for patients with chronic disease. The association with practice size and DGP activities suggests that practice organisation and systems are important in the provision of good care for patients with chronic disease.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".