Routine Medical Emergency Team Assessments of Patients Discharged From the PICU
Bibliographic record
Abstract
OBJECTIVE: This study describes one follow-up program in the Ontario Rapid-Response System project consisting of routine medical emergency team visits of patients discharged from the PICU consisting of two planned visits within 48 hours following discharge. Study purpose was to describe interventions provided and the patient characteristics associated with medical emergency team utilization. DESIGN: Retrospective cohort study. SETTING: Tertiary Pediatric Hospital, Children's Hospital of Eastern Ontario, Ottawa, Canada. PATIENTS: Discharged pediatric patients from PICU. INTERVENTIONS: Data over 41 months were obtained from a prospectively maintained rapid-response system database. Major medical emergency team support was defined as an early unplanned visit, intervention, or readmission during the follow-up period. MEASUREMENTS AND MAIN RESULTS: Interrupted time-series analysis comparing the 2 years preceding rapid-response system implementation with the subsequent 4 years demonstrated a statistically significant immediate change in PICU readmission rate (-5.5%, p = 0.0001). There were 1,805 patients followed after PICU discharge. During the 48-hour planned follow-up period, 4% of patients received an unplanned medical emergency team visit and 13% received an active intervention. Analysis of the first medical emergency team visit identified that 10% received major medical emergency team support. After the initial visit, 6% of patients received major medical emergency team support with predictive characteristics being an unplanned first visit (odds ratio, 3.7; 95% CI, 1.6-8.5) or an intervention during the first visit (odds ratio, 3.5; 95% CI, 2.1-5.8). Multiple diseased organs were associated with major medical emergency team support after the initial visit for recent surgical patients (odds ratio, 3.0 vs 1.2; p = 0.03). CONCLUSIONS: Routine medical emergency team visits following PICU discharge reduced the risk of early readmission. Our results suggest that one in seven patients in the follow-up program receive major medical emergency team support. We suggest a follow-up program with at least one routine medical emergency team visit within the first 24 hours of discharge with a second planned visit reserved for complex postsurgical patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".