Diabetic patients with prior specialist care have better glycaemic control than those with prior primary care
Bibliographic record
Abstract
OBJECTIVE: To compare glycaemic control, as reflected in the A1c level, of diabetic patients with primary care vs. with specialist care. METHODS: The study used administrative data from eastern Ontario, Canada, and a database containing the results of all A1c tests from this region between 1 September 1999 and 1 September 2000. To avoid referral bias, diabetic patients with an index specialist visit were selected and separated into those with exclusively primary care previously (n = 974) and those with prior specialist care (n = 3533). We compared A1c levels measured within 30 days of the index visit and hence attributable to the prior care. To control for confounding between the groups, both multiple linear regression and propensity score-based matching were used. RESULTS: After controlling for confounders, patients with prior specialist care had significantly lower A1c levels (P < 0.0001). Other predictors of lower A1c included older age, shorter diabetes duration, rural residence and higher neighbourhood income. In propensity score-matched cohorts, the A1c level was 8.3 +/- 2.0% with prior primary care vs. 7.9 +/- 1.6% with prior specialist care (P < 0.0001). CONCLUSIONS: Specialist care prior to the index visit was associated with a lower A1c level than prior primary care. This difference would result in reductions in diabetes complications for patients with ongoing specialist care.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".