Effects of rapid response systems on clinical outcomes: Systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: A rapid response system (RRS) consists of providers who immediately assess and treat unstable hospitalized patients. Examples include medical emergency teams and rapid response teams. Early reports of major improvements in patient outcomes led to widespread utilization of RRSs, despite the negative results of a subsequent cluster-randomized trial. PURPOSE: To evaluate the effects of RRSs on clinical outcomes through a systematic literature review. DATA SOURCES: MEDLINE, BIOSIS, and CINAHL searches through August 2006, review of conference proceedings and article bibliographies. STUDY SELECTION: Randomized and nonrandomized controlled trials, interrupted time series, and before-after studies reporting effects of an RRS on inpatient mortality, cardiopulmonary arrests, or unscheduled ICU admissions. DATA EXTRACTION: Two authors independently determined study eligibility, abstracted data, and classified study quality. DATA SYNTHESIS: Thirteen studies met inclusion criteria: 1 cluster-randomized controlled trial (RCT), 1 interrupted time series, and 11 before-after studies. The RCT showed no effects on any clinical outcome. Before-after studies showed reductions in inpatient mortality (RR = 0.82, 95% CI: 0.74-0.91) and cardiac arrest (RR = 0.73, 95% CI: 0.65-0.83). However, these studies were of poor methodological quality, and control hospitals in the RCT reported reductions in mortality and cardiac arrest rates comparable to those in the before-after studies. CONCLUSIONS: Published studies of RRSs have not found consistent improvement in clinical outcomes and have been of poor methodological quality. The positive results of before-after trials likely reflects secular trends and biased outcome ascertainment, as the improved outcomes they reported were of similar magnitude to those of the control group in the RCT. The effectiveness of the RRS concept remains unproven.
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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 | 0.030 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".