Monitoring, treatment and control of blood glucose and lipids in Ontario First Nations people with diabetes
Bibliographic record
Abstract
BACKGROUND: Indigenous people worldwide are disproportionately affected by diabetes and its complications.We aimed to assess the monitoring, treatment and control of blood glucose and lipids in First Nations people in Ontario. METHODS:We conducted a longitudinal population-based study using administrative data for all people in Ontario with diabetes, stratified by First Nations status.We assessed age-and sex-specific rates of completion of recommended monitoring for low-density lipoprotein (LDL) and glycated hemoglobin (A 1c ) from 2001/02 to 2014/15.We used data from 2014/15 to conduct a cross-sectional analysis of rates of achievement of A 1c and LDL targets and use of glucose-lowering medications. R E S U L T S :T h e s t u d y i n c l u d e d 22 240 First Nations people and 1 319 503 other people in Ontario with diabetes.Rates of monitoring according to guidelines were 20%-50% for A 1c and 30%-70% for lipids and were lowest for younger First Nations men.The mean age-and sex-adjusted A 1c level was higher among First Nations people than other people (7.59 [95% confidence interval (CI) 7.57 to 7.61] v. 7.03 [95% CI 7.02 to 7.03]).An A 1c level of 8.5% or higher was observed in 24.7% (95% CI 23.6 to 25.0) of First Nations people, compared to 12.8% (95% CI 12.1 to 13.5) of other people in Ontario.An LDL level of 2.0 mmol/L or less was observed in 60.3% (95% CI 59.7 to 61.6) of First Nations people, compared to 52.0% (95% CI 51.1 to 52.9) of other people in Ontario.Among those aged 65 or older, a higher proportion of First Nations people than other Ontarians were using insulin (28.1% v. 15.1%), and fewer were taking no medications (28.3% v. 40.1%). INTERPRETATION:As of 2014/15, monitoring and achievement of glycemic control in both First Nations people and other people in Ontario with diabetes remained suboptimal.Interventions to support First Nations patients to reach their treatment goals and reduce the risk of complications need further develop ment and study.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".