Anticipating Pediatric Patient Transfers From Intermediate to Intensive Care
Bibliographic record
Abstract
OBJECTIVES: To explore characteristics of patients who were admitted to the intermediate care (IC) unit at a tertiary academic institution. In particular, we sought to compare the characteristics of IC patients who were transferred with the characteristics of those who were not transferred to PICU care and evaluate predictors of patient transfer. METHODS: Data were collected on all admitted IC patients between July 2016 and June 2018. Patients whose index IC admission was from the PICU were excluded. Data collected included demographics and physiologic characteristics: heart rate, respiratory rate, temperature, oxygen therapy, as well as Bedside Pediatric Early Warning System (BPEWS) score. RESULTS: In this time period, 427 eligible patient visits occurred, with 66 patients (15.46%) being transferred to the PICU. Patients were commonly transferred early in their IC course (1.41 days into admission [0.66–3.87]); transferred patients had higher median admission BPEWS scores (7 [4.25–9] vs 5 [3–7]; P < .01). In the univariate analysis, no individual physiologic characteristic was predictive for transfer. In the multivariate analysis, BPEWS (P < .001) and need for any form of respiratory support (P = .04) were significant predictive factors for transfer (R2 = 0.56). CONCLUSIONS: The need for close monitoring of physiologic parameters remains paramount, especially in the first 48 hours of admission, in predicting the need for transfer from the IC to PICU. The need for any form of respiratory support is predictive of transfer. Situational awareness and assessment including BPEWS score is of critical importance.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".