BRINGING ICU CARE TO BEDSIDE: HAVE CRITICAL CARE RESPONSE TEAMS IMPROVED THE QUALITY OF PATIENT CARE IN ONTARIO?
Bibliographic record
Abstract
Background Critical Care Response Teams (CCRTs) are multidisciplinary teams of critical care clinicians with expertise in providing speedy clinical review and intervention to deteriorating patients. In 2006, Critical Care Services Ontario introduced a CCRT program in 31 Ontario acute care hospitals (27 adult, 4 pediatric) to improve quality of care for patients. Objectives To assess the impact of intensivist-led CCRTs on adult patient outcomes in 27 sites after eight years of implementation. Methods The study used a mixed method design, using CCRT data from the provincial Critical Care Information System, hospital-reported data on cardiac arrest and mortality rates, and semi-structured interviews. The quantitative findings are presented here. Longitudinal data analysis was conducted using data from 2007–2015. Trend tests assessed the significance of the implementation time across the study period. Linear regressions modeled the relationship between the two outcome variables (non-ICU cardiac arrest and hospital mortality), and CCRT implementation time, adjusting for age, gender, and type of site (teaching or community). Results Cardiac arrest rates decreased over time from 1.9 in FY2007/08 to 1.5 per 1000 admissions in FY2014/15 and the trend test was significant (ßtime=−0.11 [−0.19, −0.03]). Hospital mortality rates also decreased over time (39.8 per 1000 admissions in FY 2007/08 to 37.7 in FY 2014/15), the trend test was significant only for teaching sites (ßtime=−0.22 [−0.29, −0.14]). Conclusions Our results show that intensivist led-CCRTs improve patient outcomes and have strengthened critical care services system. Based on these positive findings, nurse-led model for CCRTs is currently being piloted in community hospitals.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".