Patterns of diabetes testing for older adults without diabetes in Ontario's nursing homes: A population‐based study
Bibliographic record
Abstract
BACKGROUND: Asymptomatic diabetes testing may be of limited value for older nursing home residents, but most diabetes guidelines lack upper-age cutoffs for screening cessation. We evaluated patterns of glycated hemoglobin (HbA1c) and serum blood glucose (SBG) testing among older residents without diabetes in Ontario, Canada. METHODS: This population-based retrospective cohort study used provincial health administrative data from ICES to identify older nursing home residents in Ontario without diabetes between January 1, 2015 and December 31, 2018. We examined HbA1c and glucose testing rates overall, by age, sex, and near end-of-life. The number of tests needed to identify one case of diabetes (using HbA1c thresholds of 6.5% and 8.0%) were also calculated. RESULTS: Among 102,923 older nursing home residents (70.3% women; average age 85.6 ± SD 7.7 years), 46.1% of residents received ≥1 HbA1c test over an average follow-up period of 2.15 (± SD 1.49) years, and 18.2% of these tested residents received ≥4 HbA1c tests. The crude HbA1c testing rate was 52.6 tests/100 person-years (95% CI 52.3-52.9). Testing rates among residents aged ≥80 years was 50.7 HbA1c tests/100 person-years (95% CI 50.4-51.0), and 47.8 tests/100 person-years (95% CI 46.5-49.0) among residents near end-of-life. The number of tests to identify a case of diabetes (HbA1c ≥ 6.5%) was 44, while the number of tests to identify a case of actionable diabetes (HbA1c ≥ 8%) was 310. Less than 1% of residents with an HbA1c test met criteria for actionable diabetes. CONCLUSIONS: Nursing home residents without diabetes receive frequent diabetes testing, with high testing rates even in residents over 80 years old and residents near end-of-life. The high number of tests needed to identify a case of actionable diabetes highlights the urgent need to re-evaluate diabetes testing practices in nursing homes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".