The Impact of Implementation of an ICU Consult Service on Hospital-Wide Outcomes and ICU-Specific Outcomes
Bibliographic record
Abstract
BACKGROUND: Rapid response teams (RRTs) were developed to promote assessment of and early intervention for clinically deteriorating hospitalized patients. Although the ideal composition of RRTs is not known, their implementation does require significant resources. OBJECTIVE: To test the effectiveness of a dedicated daytime/weekday intensive care unit (ICU) consult service without formal training of ward teams. METHODS: Pre- and postintervention study with weekends/nights during implementation period acting as a concurrent control. SETTING: An adult tertiary care university center in Montreal without an RRT. INTERVENTION: A daytime/weekday ICU consult service with a dedicated intensivist. RESULTS: Total hospital mortality rate did not differ between the control and the implementation period (6.65% vs 6.60%; P = .84). The hospital code blue rates also did not differ (1.21/1000 vs 1.14/1000 patient days; P = .58). In contrast, 30-day mortality of patients admitted to the ICU following an ICU consult decreased (39% vs 24% P = .01). Multivariate analysis confirmed this effect on 30-day mortality (odds ratio for implementation period: 0.53 [95% confidence interval: 0.33-0.85] P = .009). The 14-day ICU readmission rate was reduced with the intervention (5.1% vs 4.1%; P < .001). The effect on 30-day mortality and ICU readmissions were only present during daytime/weekdays. CONCLUSION: Implementation of an ICU consult service without any formal afferent limb training was associated with decreased mortality and 14-day readmission rates of patients admitted to the ICU. In contrast, hospital-wide mortality and code blue rates were unaffected.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".