[Triage evaluation making in a pediatric emergency department of a tertiary hospital].
Bibliographic record
Abstract
OBJECTIVE: Evaluation triage level assignments depending level of the professionals' education and experience in the unit. METHOD: This was a retrospective and observational study to triages making from January to March 2012 in Pediatric Emergency Department of tertiary hospital in Madrid. The collection data included variables from Pediatric Canadian Triage with five levels, triage tool using in the unit. RESULTS: 6443 triages were evaluated. The most common mistakes was: not to register pain level, 1445 (22.4%); not to register hydration level, 377 (5.9%); principal symptoms inappropriate, 232 (3.6%). Didn't indicate pain level 140 (5.6%) nurses with 12 hour formal training on triage; 492 (14.5%) with training in the unit, and 92 (16.3%) without training in the last year (p < 0.001). Among the nurses working in the unit more than 7 years did not register pain level 472 (12.3%), identified inappropriate principal symptoms 197 (5%) and did not register hydration level 296 (7.7%). CONCLUSIONS: The triage education favors better adaptation in the triage assignment. The most common errors are: not to register level pain and hydration when it's needed for the principal symptoms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".