Blood Glucose Control Among Critically Ill Patients with Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To evaluate the incidence of hypoglycemia, hyperglycemia and blood glucose (BG) variability in brain-injured patients and their association with clinical outcomes. METHODS: Retrospective cohort study of brain-injured patients admitted to an 11-bed neurosciences intensive care unit (ICU) from January 1 to December 31, 2003. RESULTS: We included 606 patients. Mean age was 52.3 years, 60.6% were male, 11.9% had diabetes mellitus, and 64% were post-operative. Seventy-five (12.4%) received intensive insulin therapy (IIT) for a median (IQR) 72 (24-154) hours. Hypoglycemia and hyperglycemia occurred in 4.6% (96.4% receiving IIT) and 9.6% (77.6% receiving IIT). Median number of episodes per patient was 3 (75% with > or = 2) and 4 (81% with > or = 2) for hypoglycemia and hyperglycemia. Variable glycemic control occurred in 3.8% (100% receiving IIT) with median number of 13 episodes per patient. In-hospital mortality was 16.7%, median (IQR) ICU and hospital lengths of stay were 2 (1-5) and 8 (3-19) days. Hypoglycemia, hyperglycemia and BG variability showed non-significant but consistent associations with hospital mortality and prolonged lengths of ICU and hospital stay. The rate of recurrence of episodes showed stronger and significant associations with outcome, in particular for BG variability and hyperglycemia. CONCLUSIONS: Hypoglycemia, hyperglycemia and BG variability are relatively common in brain-injured patients and are associated with IIT. An increased frequency of episodes, in particular for BG variability and hyperglycemia, was associated with greater risk of both hospital death and prolonged duration of stay.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".