Impaired renal function modifies the risk of severe hypoglycaemia among users of insulin but not glyburide: a population-based nested case–control study
Bibliographic record
Abstract
BACKGROUND: Little evidence justifies the avoidance of glyburide in patients with impaired renal function. We aimed to determine if renal function modifies the risk of hypoglycaemia among patients using glyburide. METHODS: We conducted a nested case-control study using administrative records and laboratory data from Ontario, Canada. We included outpatients 66 years of age and older with diabetes mellitus and prescriptions for glyburide, insulin or metformin. We ascertained hypoglycaemic events using administrative records and estimated glomerular filtration rates (eGFR) using serum creatinine concentrations. RESULTS: From a cohort of 19,620 patients, we identified 204 cases whose eGFR was ≥ 60 mL/min/1.73 m(2) (normal renal function) and 354 cases whose eGFR was < 60 mL/min/1.73 m(2) (impaired renal function). Compared to metformin, glyburide is associated with a greater risk of hypoglycaemia in patients with both normal [adjusted odds ratio (OR) 9.0, 95% confidence interval (95% CI) 4.9-16.4] and impaired renal function (adjusted OR 6.0, 95% CI 3.8-9.5). We observed a similar relationship when comparing insulin to metformin; the risk was greater in patients with normal renal function (adjusted OR 18.7, 95% CI 10.5-33.5) compared to those with impaired renal function (adjusted OR 7.9, 95% CI 5.0-12.4). Tests of interaction showed that among glyburide users, renal function did not significantly modify the risk of hypoglycaemia, but among insulin users, impaired renal function is associated with a lower risk. CONCLUSIONS: In this population-based study, impaired renal function did not augment the risk of hypoglycaemia associated with glyburide use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".