Compliance With Evidence-Based Processes of Care After Transitions Between Staff Intensivists
Bibliographic record
Abstract
OBJECTIVES: We sought to evaluate the impact of transitions of care among staff intensivists on the compliance with evidence-based processes of care. DESIGN: Cohort study using data from the Toronto Intensive Care Observational Registry. SETTING: Seven academic ICUs in Toronto, Ontario. PATIENTS: Critically ill mechanically ventilated adult patients. INTERVENTIONS: We explored the effects of the weekly transition of care among staff intensivists on compliance with three evidence-based processes of care (spontaneous breathing trials, lung-protective ventilation, and neuromuscular blocking agents). Two practices that are less guided by evidence (early discontinuation of antibiotics and extubation attempts) served as positive controls. We conducted the analysis using generalized estimating equations to account for clustering at the patient level. MEASUREMENTS AND MAIN RESULTS: The cohort consisted of 10,570 patients admitted between June 2014 and August 2018. Compliance varied for each practice (63.6%, 42.5%, and 21.1% for lung-protective ventilation, spontaneous breathing trials, and neuromuscular blockade, respectively). There was no effect of transitions of care on compliance with spontaneous breathing trials (odds ratio, 1.00; 95% CI, 0.95-1.07), lung-protective ventilation (odds ratio, 1.07, 95% CI, 0.90-1.26), or neuromuscular blockade use (odds ratio, 0.95; 95% CI, 0.75-1.20). However, early antibiotic discontinuation was more likely (odds ratio, 1.23; 95% CI, 1.06-1.42) and extubation attempts were less frequent (odds ratio, 0.77; 95% CI, 0.65-0.93) after a transition of care. CONCLUSIONS: We observed no significant impact of transitions of care between individual staff physicians on evidence-based processes of care for mechanically ventilated adult patients. However, transitions were associated with a lower likelihood of extubation and higher odds of earlier discontinuation of antibiotics.
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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 | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".