Diabetes self‐management education is not associated with a reduction in long‐term diabetes complications: an effectiveness study in an elderly population
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The efficacy of diabetes self-management education on glycaemic control, self-care behaviour and knowledge has been established by short-term studies in experimental settings. The objective of this study was to assess its effectiveness to improve quality of care and reduce the risk of long-term diabetes complications in unselected older patients with recently diagnosed diabetes in routine clinical care. METHODS: Using population-level health care administrative databases and registries, all patients aged ≥66 years in Ontario, Canada with diabetes for <5 years were identified. Self-management education programme attendees (n = 8485) in 2006 were matched with non-attendees using high-dimensional propensity scores, creating extremely well-balanced study arms. Quality of care measures and the long-term risk of diabetes complications were compared. RESULTS: Self-management programme attendees were more likely than non-attendees to achieve process measures of quality of care such as retinal screening examinations (75.3% versus 70.3%, adjusted relative risk 1.05, 99% confidence interval 1.03-1.08), and ≥2 glycated haemoglobin tests (57.5% versus 53.3%, adjusted relative risk 1.08, 99% confidence interval 1.05-1.11). However, with a median follow-up of 5.3 years, diabetes complications and mortality were not different between arms. CONCLUSIONS: In real-world clinical care, self-management education for older patients with recently diagnosed diabetes was associated with modest improvements in quality of care, but no reductions in long-term clinical events.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".