Development of a Provincial initiative to improve glucose control in critically ill patients
Bibliographic record
Abstract
OBJECTIVE: To describe the development, implementation and initial evaluation of an initiative to improve glucose control in critically ill patients. DESIGN: Glucose control in critically ill patients was chosen by critical care leaders as a target for improvement. This was an observational study to document changes in processes and measures of glucose control in each intensive care unit (ICU). ICU nurse educators were interviewed to document relevant changes between April 2012 and April 2016. SETTING: 16 ICUs in British Columbia, Canada. PARTICIPANTS: ICU leaders. INTERVENTION(S): A community of practice (CoP) was formed, guidelines were adopted, two learning sessions were held, and an electronic system to collect data was created. Then, each ICU introduced their own educational and process interventions. MAIN OUTCOME MEASURE(S): Average hyperglycemic index (area under the curve of serum glucose concentration versus time above the upper limit (10 mmol/l) divided by time on insulin infusion), number of hypoglycemic events (<3.5 mmol/l) divided by time on insulin infusion and standardized mortality rate (actual/predicted hospital mortality) for each 3-month period. RESULTS: Although there were some isolated points and short trends that indicated special cause variation, there were no major trends over time and no obvious association with any of the process changes for each hospital. However, the average hyperglycemic index was higher in some of the smaller hospitals than in the larger hospitals. CONCLUSIONS: In this, 4-year observation of glucose control in ICUs within a CoP, the lack of sustained improvement suggests the need for more active and durable interventions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".