The cost of major comorbidity in people with diabetes mellitus.
Bibliographic record
Abstract
BACKGROUND: People with diabetes mellitus are more likely to have cardiovascular, renal and ophthalmic comorbidity than those without diabetes. Information on the economic impact of diabetes and its complications on the Canadian health care system is limited. METHODS: To estimate health care expenditures for diabetes and its major complications, we identified people with diabetes in 1996 in Saskatchewan, using the administrative databases of Saskatchewan Health. We grouped utilization and expenditure data for prescription drugs, physician services, hospitalizations, day surgery procedures and dialysis services according to cardiovascular, renal and ophthalmic services, according to billing codes and the American Hospital Formulary Services classification for prescription drugs. RESULTS: Of the 38 124 people identified (48.5% female and 9.7% registered Indians), 46.6% had cardiovascular-related records, 19.8% ophthalmic-related records and 6.6% renal-related records. Registered Indians had significantly fewer (p < 0.001) cardiovascular-related records than the rest of the diabetic population (35.1% v. 47.9%, respectively) but more renal- related records (11.7% v. 6.0%, respectively). The total 1996 Saskatchewan Health expenditure for the study group, within the observed categories, was estimated to be $134.3 million, of which $35.5 million (26.4%) was for cardiovascular-related services, $10 million (7.5%) for renal-related services and $3.3 million (2.5%) for ophthalmic-related services. INTERPRETATION: In 1996, 36.4% of health care expenditures for people with diabetes was attributable to major comorbidity. Actions to prevent or control such comorbidity will yield significant cost savings.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".