Diabetes mellitus related morbidity, risk of hospitalization and disability.
Bibliographic record
Abstract
OBJECTIVE: To investigate the rates of complications, hospitalizations and disabilities attributable to type 1 and type 2 diabetes mellitus (DM) combined, unless otherwise noted. METHODOLOGY: Risk assessment of DM-related morbidity, hospitalizations and disabilities using data from the medical literature and health statistics on the population. Calculation of morbidity, hospitalization, and disability ratios (MbR, HR, DR) will allow comparison of observed rates in people with DM to those reported in the nondiabetic population. RESULTS: MbRs vary according to the morbid condition studied: approximately 300% at age 45-64 years for ischemic heart disease, 533% for coronary heart disease or stroke, 226% to 388% for chronic heart failure, 560% for peripheral vascular disease, 380% for neuropathy at age 35-74 years, 890% to 2225% for lower limb amputations, 1458% to 3287% for end-stage renal disease. For ocular complications: cataracts, 165% to 232%; glaucoma 140% to 330%; trouble seeing, 180% to 231%; blindness at age > or = 65 years, 517%. Higher values are noted at younger ages. HR: 200% to 409%. DR: 217% to 328%. CONCLUSION: Among diseases, DM is one of the leading and growing causes of hospital admission and disability. Precise risk assessment of morbidity is essential for realistic underwriting of health and disability insurance.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".