Advanced Practice Providers as Leaders of a Rapid Response Team: A Prospective Cohort Study
Bibliographic record
Abstract
In view of the shortage of medical staff, the quality and continuity of care may be improved by employing advanced practice providers (APPs). This study aims to assess the quality of these APPs in critical care. In a large teaching hospital, rapid response team (RRT) interventions led by APPs were assessed by independent observers and intensivists and compared to those led by medical residents MRs. In addition to mortality, the MAELOR tool (assessment of RRT intervention), time from RRT call until arrival at the scene and time until completion of clinical investigations were assessed. Process outcomes were assessed with the crisis management skills checklist, the Ottawa global rating scale and the Mayo high-performance teamwork scale. The intensivists assessed performance with the handoff CEX recipient scale. Mortality, MAELOR tool, time until arrival and clinical investigation in both groups were the same. Process outcomes and performance observer scores were also equal. The CEX recipient scores, however, showed differences between MRs and APPs that increased with experience. Experienced APPs had significantly better situational awareness, better organization, better evaluations and better judgment than MRs with equal experience (p < 0.05). This study shows that APPs perform well in leading an RRT and may provide added quality over a resident. RRTs should seriously consider the deployment of APPs instead of junior clinicians.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".