Glycaemic Fluctuation Predicts Mortality in Critically Ill Patients
Bibliographic record
Abstract
Growing evidence suggests that glycaemic variability increases diabetic complications. However, the significance of glycaemic variability in critically ill patients remains unclear. We evaluated the predictors of glycaemic fluctuation and its association with critical care outcomes. This is a nested-cohort study within a clinical trial in which 523 patients at a medical surgical intensive care unit were randomised to either intensive insulin therapy (target glycaemic control: 4.4 to 6.1 mmol/l) or conventional insulin therapy (target control: 10.0 to 11.1 mmol/l). Glycaemic fluctuation was defined as the mean difference between the highest and lowest daily blood glucose. Patients were divided into wide and narrow fluctuation groups according to the median glycaemic fluctuation (6.0 mmol/l). The association between glycaemic fluctuation and different intensive care unit outcomes was studied. Predictors of glycaemic fluctuation were age (odds ratio for each year increment 1.03, 95% confidence interval 1.02 to 1.05), diabetes mellitus (odds ratio 3.00, 95% confidence interval 1.74 to 5.17), and daily insulin dose (odds ratio for each unit increment 1.04, 95% confidence interval 1.03 to 1.05). Similar levels of glucose fluctuation were observed in intensive insulin therapy and conventional insulin therapy patients. Wide glycaemic fluctuation was associated with higher mortality (22.2 vs. 8.4%, P < 0.001). Glycaemic fluctuation was identified as an independent predictor of intensive care unit mortality (odds ratio per mmol 1.08, 95% confidence interval 1.00 to 1.18) and hospital mortality (odds ratio per mmol 1.09, 95% confidence interval 1.02 to 1.17) using multivariate logistic regression analysis. In conclusion, wide glycaemic fluctuation is an independent predictor of mortality in critically ill patients. Whether reducing glycaemic fluctuation would lead to better outcomes needs further evaluation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".