The Canadian Paediatric Triage and Acuity Scale algorithm for interfacility transport
Bibliographic record
Abstract
OBJECTIVE: Determining pediatric severity of illness in referring centers may be useful for establishing appropriate patient disposition and interfacility transport. For this retrospective review, the authors evaluated the Canadian Paediatric Triage and Acuity Scale (PaedCTAS) tool in regards to individual patient disposition and outcomes. METHODS: A disposition score using the PaedCTAS algorithm was retrospectively calculated from referring center data at the time our transport team was consulted. Data included children < 17 years transported to our tertiary pediatric center between April 2013 and March 2014. Patients were excluded if transported because of elective or planned interventions, investigations, and/or treatment. RESULTS: A total of 194 pediatric patients were identified, with 49 requiring a pediatric intensive care unit (PICU) admission. A PaedCTAS assessment of 1 was the only transport characteristic evaluated that was significantly associated (odds ratio [OR] 6.15; p < 0.0001) with PICU admissions, with an area under the receiver-operating characteristic curve of 0.72 (95% CI 0.64, 0.77). On multivariate analysis, a PaedCTAS assessment of 1 was also associated with a length of hospital stay greater than 3 days (OR 1.81; 95% CI 0.99, 3.31; p = 0.05). CONCLUSIONS: A PaedCTAS assessment of 1 may be a reasonable predictor for PICU admissions and longer hospitalizations when calculated in referral centers at time of pediatric transport consultation. PaedCTAS assessments may provide useful adjuvant information for specialized pediatric transport programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".