Specialist‐led diabetes registries and predictors of poor glycemic control in type 2 diabetes: Insights into the functionally refractory patient from the <scp>LMC D</scp>iabetes <scp>R</scp>egistry database
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to explore features associated with glycemic control in type 2 diabetes (T2D) patients undergoing care by specialist clinics. METHODS: Literature searches identified diabetes registries whose databases recorded outcomes of specialist care. The LMC Diabetes Registry database (n = 58 280; LMC) was queried to identify patients with T2D who had been seen in a defined 14-month period. Logistic regression modeling was used to identify predictors of glycemic control in these patients. Poor glycemic control was defined as HbA1c ≥9.0% (75 mmol/mol) despite specialist care for ≥1 year. RESULTS: Few published registry-based studies have discussed glycemic control and outcomes of specialist care for T2D. Among 10 590 LMC patients with T2D, mean HbA1c was 7.6% (60 mmol/mol), with 38% of patients meeting the Canadian Diabetes Association target of ≤7.0% (53 mmol/mol). Overall, 15% showed poor glycemic control with persistent HbA1c ≥9.0% (75 mmol/mol); among insulin-treated patients (n = 3856), 28% met this criterion. Patient characteristics independently associated with poor glycemic control included early age of onset, the number of diabetes education program visits, the number of oral therapies, and insulin use. CONCLUSIONS: Type 2 diabetes patients with poor glycemic control are found disproportionately in referral specialist care clinics. These functionally refractory patients demonstrate features that may assist in predicting their potential outcome, and may represent a group with specific barriers to care. Specialist patient registries, such as the LMC Diabetes Registry, may provide critical information regarding this cohort.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".