Knowledge translation in critical care: Factors associated with prescription of commonly recommended best practices for critically ill patients*
Bibliographic record
Abstract
OBJECTIVE: To describe prescription rates of commonly recommended best practices (clinical interventions with a strong base of evidence supporting their implementation) for critically ill patients and determine factors associated with increased rates of prescription. DESIGN: A retrospective observational study. SETTING: A university-affiliated medical-surgical-trauma intensive care unit over a 1-yr period. PATIENTS: One hundred randomly selected critically ill patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among the best practices studied, there was great variability in the proportion of patients eligible (median 36.5%, range 10% to 100%) and the proportion without contraindication (32.5%, range 10% to 86%) for each practice. The median rate of prescription of best practices for eligible patients was 56.5%, with a range from 8% to 95%. There was greater prescription of best practices when standard admission orders included an option to prescribe them (p = .048). Among those practices with standard admission orders, there was greatest prescription for practices additionally having a specialty consultation service (p = .004). There was an inverse association between severity of illness and prescription of best practices (p = .001): Sicker patients were less likely to be prescribed best practices. CONCLUSIONS: There may be substantial variability in the acceptance and prescription of commonly recommended best practices for critically ill patients. Standard order sets and focused specialty consultation may improve knowledge translation and prescription of best practice.
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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 | 0.009 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".