Variability in Physician Care Practices for Glucose Treatment in Stroke Patients
Bibliographic record
Abstract
BACKGROUND: Hyperglycemia is noted in up to 60% of stroke patients. Practice guidelines recommend glucose monitoring following stroke but provide few management recommendations. We examined physician care practices for glucose management in stroke patients. METHODS: Emergency physicians, family physicians, general internists, intensive care specialists and neurologists in Ontario comprised the study population. A mailed, self-administered survey inquired about glucose management practices. Proportions of responses for survey questions were determined. Chi-square analysis was used for comparing physician groups. RESULTS: Surveys were mailed to 2,280 physicians; 26.8% returned surveys. There were 278 respondents who reported providing stroke patient care. For physicians treating glucose in stroke patients, 16.6% targeted glucose 4.0-6.0 mmol/l, 52% targeted 6.1-8.0 mmol/l, 13.6% targeted 8.1-12.0 mmol/l, 0.8% targeted 12.1-15.0 mmol/l, and 7.5% were unsure. Comparing specialties, 32% of intensivists, 17.5% of neurologists, 13% of general internists, 14% of emergency physicians, and 0% of family physicians reported targeting 4.0-6.0 mmol/l (p=0.026). Overall, 44% reported aiming for target glucose within 12 hours and 77% within 24 hours from hospital presentation. Intensive care specialists treated glucose most aggressively, including 20% treating, with insulin infusion, patients with no diabetes and initial glucose 6.0-8.0 mmol/l. Emergency physicians were most conservative when treating glucose in stroke patients. CONCLUSION: There is variability in the aggressiveness of glucose management in stroke patients by different physician specialty groups, reflecting the lack of evidence available to guide hyperglycemia management in this setting. These results highlight an important gap in knowledge and recommendations for stroke patient care that must be addressed to ensure optimal patient outcomes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".