Intensive nursing work schedules and the risk of hypoglycaemia in critically ill patients who are receiving intravenous insulin
Bibliographic record
Abstract
RATIONALE: Nurses in the intensive care unit (ICU) commonly work frequent 12 h shifts, potentially leading to fatigue and reduced vigilance. The authors hypothesised that rates of hypoglycaemia in patients receiving an insulin infusion would be associated with the intensity of work of the bedside nurse in the preceding 72 h. METHODS: The authors identified ICU patients who had hypoglycaemia (glucose ≤3.5 mmol/l, 63 mg/dl) between October 2006 and June 2007. The number of shifts worked in the previous 72 h was calculated for the nurse caring for the patient when the event occurred (case shift). For each case shift, the authors identified up to three control shifts (24, 48 and 72 h before the event in the same patient) and calculated the number of shifts worked by nurses on these shifts in the previous 72 h. Conditional logistic regression was used to determine whether the number of shifts worked was associated with hypoglycaemia. RESULTS: There were 41 events (32 patients). Each additional shift worked in the previous 72 h was associated with a significantly increased risk of hypoglycaemia (OR = 1.65/shift, 95% CI 1.01 to 2.68, p = 0.04) after controlling for nurse age and experience. The association was greater for the 23 events associated with an error in management according to the insulin protocol (OR = 2.93/shift, 1.15 to 7.44, p = 0.02) compared with events not associated with an error (OR = 1.34/shift, 0.73 to 2.45, p = 0.34). CONCLUSIONS: Intensive nursing work schedules are associated with hypoglycaemic events in ICU patients.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".