Patient profiles at a centralized, urban, diabetes education centre.
Bibliographic record
Abstract
BACKGROUND: Little is known about the characteristics of patients attending diabetes education centres (DECs). To address this knowledge gap, we examined the clinical characteristics of patients referred to a centralized urban DEC. METHODS: Using a clinically detailed patient registry, we studied the profiles of 1459 patients seen in an urban DEC, and compared patients referred to the DEC by family physicians (FPs) to those referred by other physicians (usually specialists), and patients referred to the DEC for the first time to those who had been referred a number of times (multiply-referred patients). RESULTS: Among patients with a known source of referral, 73% were referred by their FP and 27% by a physician other than the FP. Eighty-seven percent of patients were being referred for the first time, and 13% had previous referrals. Blood glucose control at the time of referral was poorer for non-FP referrals and for multiply-referred patients. Patients in the former subgroup were more likely taking insulin when referred (38% v. 12%, p < 0.000), to have type 1 diabetes (19% v. 8%, p < 0.000) and to be referred for insulin initiation (12% v. 2%, p < 0.000) than were FP referrals. Meanwhile, multiply-referred patients were younger (51.9 v. 56.1 yr, p < 0.000) and were more likely to be female (59% v. 46%, p = 0.001) than were patients referred only once. INTERPRETATION: Source of referral (FP v. non-FP) and presence or absence of previous referrals define unique DEC patient subgroups. Attention to the relative size and service needs of these subgroups is relevant to the planning of diabetes services.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".