Characteristics and predictors of high acuity pediatric patients presenting to a regional community healthcare system who require transfer to a tertiary pediatric center.
Bibliographic record
Abstract
Purpose: To identify the characteristics at triage of high acuity pediatric patients who presented to community emergency departments and determine predictors for those who require transfer to a tertiary care pediatric center. Patients and methods: We conducted a retrospective study of all pediatric Canadian Triage and Acuity Scale (CTAS) I patients presenting to five semirural hospital sites from January to December 2018. Univariate tests were used to identify significant predictors for transfer based on age, gender, Pediatric Early Warning Score (PEWS) score and presenting complaint. A multivariate model was developed based on backward selection from the significant factors from the univariate analysis to identify predictors for transfer. Results: There were 1,137 subjects with an average age of 5.08 years (SD: 5.03) of whom, 559 (49.2%) were males. Sixty patients (5.3%) were transferred to a tertiary care center (60.9% <4 years). A PEWS score ≥3 (OR 3.005, 95% CI 1.623–5,563), presenting with trauma (OR 6.617, 95% CI 2.820–15-531), mental health issues (OR 5.131, 95% CI 1.444–18.232), or neurological issue (OR 3.057, 95% CI 1.355–6.896) were associated with transfer. Patients with fever (OR 0.113, 95% CI 0.031–0.407) and respiratory symptoms (OR 0.345, 95% CI 0.142–0.840) were less likely to be transferred. Conclusion: Predictors of transfer from a community hospital to a pediatric tertiary care center were a PEWS score ≥3, trauma patients, those presenting with mental health issues, and patients with neurological symptoms. Early recognition can facilitate quicker transfer of these high acuity patients requiring tertiary care management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".