Glycemic Control in Community-Dwelling Patients with Type 2 Diabetes
Bibliographic record
Abstract
Nearly 25% of Canadians have either diabetes or prediabetes, with diabetes-associated health care costs reaching $12.2 billion in 2010.1 It has been reported that glycemic control in primary care is poor. Harris and colleagues2 conducted a study across the 10 provinces of Canada to assess the quality of care and treatment of type 2 diabetes patients in primary care settings. They reported that almost half of the patients with type 2 diabetes in primary care settings did not achieve their glycemic target (HbA1c ≤7%).2 Poor glycemic control puts diabetes patients at high risk of suffering from diabetes complications.3 Glycemic control testing plays an essential role not only in diabetes diagnosis,4 but it is also considered the first step in diabetes management.2 There are 3 different ways to measure glycemic control: Fasting plasma glucose (FPG) Oral glucose tolerance test (OGTT), in which the blood glucose concentration is measured 2 hours after taking a glucose solution (75 g anhydrous glucose dissolved in water) Glycated hemoglobin (HbA1c) (the 2008 Canadian Diabetes Association Guidelines recommend diabetes patients to have HbA1c ≤7%)4
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".