The association between sociodemographic and clinical characteristics and poor glycaemic control: a longitudinal cohort study
Bibliographic record
Abstract
Abstract Aims People with diabetes and poor glycaemic control are at higher risk of diabetes‐related complications and incur higher healthcare costs. An understanding of the sociodemographic and clinical characteristics associated with poor glycaemic control is needed to overcome the barriers to achieving care goals in this population. Methods We used linked administrative and laboratory data to create a provincial cohort of adults with prevalent diabetes, and a measure of HbA1c that occurred at least 1 year following the date of diagnosis. The primary outcome was poor glycaemic control, defined as at least two consecutive HbA1c measurements ≥ 86 mmol/mol (10%), not including the index measurement, spanning a minimum of 90 days. We used multivariable Cox proportional hazards models to evaluate the association between baseline sociodemographic and clinical factors and poor glycaemic control. Results In this population‐based cohort of 169 890 people, younger age was significantly associated with sustained poor glycaemic control, with a hazard ratio (HR) of 3.08, 95% CI (2.79–3.39) for age 18–39 years compared with age ≥ 75 years. Longer duration of diabetes, First Nations status, lower neighbourhood income quintile, history of substance abuse, mood disorder, cardiovascular disease, albuminuria and high LDL cholesterol were also associated with poor glycaemic control. Conclusions Although our results may be limited by the observational nature of the study, the large geographically defined sample size, longitudinal design and robust definition of poor glycaemic control are important strengths. These findings demonstrate the complexity associated with poor glycaemic control and indicate a need for tailored interventions.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".