Adoption and implementation of the original strict glycemic control guideline is feasible and safe in adult critically ill patients.
Bibliographic record
Abstract
BACKGROUND: Three trials of tight glucose control (TGC) found clinical benefit of normalization of blood glucose levels in the intensive care unit (ICU). Implementation of TGC was imperfect in subsequent trials, since attained blood glucose levels (BGLs) never reached the targets as in the original trials of TGC. We investigated whether implementation of the TGC guideline as used in the original trials of TGC is feasible and safe. METHODS: In this study 3 ICUs adopted and implemented the TGC guideline as used in the original trials of TGC using a multifaceted practice change strategy; 3 ICUs that did not change their blood glucose control guideline served as controls. TGC was practiced by physicians and nurses during the first 12-month (period-2), thereafter exclusively by nurses (period-3). Blood glucose metrics 12-month before (period-1) and 24-month after implementation of the guideline were compared. RESULTS: The analysis included 1321 in period-1, 1169 and 1006 patients in period-2, and -3, respectively, in the intervention ICUs, and 3110 patients in the control ICUs. After implementation of the new TGC guideline, patients in intervention ICUs had lower median BGLs (105 [IQR: 85-130] mg/dL vs. 119 [99-150] mg/dL in period-1, P<0.001; and vs. 113 [95-141] mg/dL in control ICUs, P<0.001). The incidence of severe hypoglycemia initially increased, but again decreased when exclusively nurses practiced TGC, and was not associated with increased mortality or morbidity. CONCLUSIONS: Implementation of the original TGC guideline is feasible and safe. Our study suggests a learning effect over time.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".