The rapid response team in outpatient settings identifies patients who need immediate intensive care unit admission: A call for policy maker
Bibliographic record
Abstract
BACKGROUND: Caregivers in the ambulatory care setting with differing clinical background could encounter a patient at high risk of deterioration. In the absence of a dedicated acute care team, the response to an unanticipated medical emergencies in these settings is likely to have a poor outcome. OBJECTIVE: To describe our experience in implementing an intensivist-led rapid response team (RRT) in the outpatient settings that identified patients who needed immediate Intensive Care Unit (ICU) admission. The effect on in hospital arrests, mortality, and ICU outcome is not the scope of this study. MATERIALS AND METHODS: This retrospective descriptive study was performed from January 1, 2009 to December 31, 2011 in a tertiary hospital. Data from hospital records were used (none from patients' records). Consent was not needed. MEASUREMENTS: Direct ICU admissions from the outpatient areas. RESULTS: There were 90 patients cared for by RRT in the outpatient's settings, 76 adult, and 14 pediatric patients. A total of12 adult patients were transferred directly to ICU. Among the patient who were transferred to the emergency department, additional four patients required to be transferred to ICU (total 16 patients [17.7%], 15 adult, and one pediatric patient). Follow-up at 24 h in the ICU showed death of one adult oncology patient (6.25%), and discharge of two patients (12.5%). Nine patients (81%) were still sick to require longer ICU stay. CONCLUSION: Intensivist-led RRT in outpatient settings identifies patients who are critically ill and in need of immediate ICU admission. Thus, an intensivist-led RRT policy in the outpatient settings needs to be implemented hospital wide.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".