Serial Clinical Scoring to Assess Transported Pediatric Patients
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to evaluate serial Transport Risk Assessment in Pediatrics (TRAP) scoring during pediatric critical care transport as a potential measure for specialized pediatric transport teams (PTTs). METHODS: This was a retrospective study with a provincial PTT from a tertiary hospital pediatric intensive care unit. All acutely ill children who were transported by the PTT between 2018 and 2019 were included in the study. The TRAP scores were measured at time of transport team arrival (TRAP1), time at arrival to tertiary center (TRAP2), and 4 hours postarrival to tertiary center (TRAP3). RESULTS: A total of 300 transports were included. Patients' mean age was 54 months, with lower respiratory tract infection (40.7%) as the most common diagnosis. There were significant differences between TRAP1-TRAP2 (P < 0.01) and TRAP1-TRAP3 (P < 0.01), but not between TRAP2-TRAP3 (P = 0.67). The most significant improvements of ΔTRAP1-TRAP2 scores were seen in septic shock (mean, 2.0; SD, 1.7). CONCLUSIONS: The TRAP scores improved following the PTTs' arrival to acutely ill children, particularly with sepsis. Serial TRAP scoring may present a system for evaluation of team performance and/or characterize disease states that are positively impacted by PTTs. Future prospective evaluation is needed to validate TRAP for this purpose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".