OCULAR POINT OF CARE ULTRASOUND (POCUS) DETECTION OF RETINOBLASTOMA IN THE PEDIATRIC EMERGENCY DEPARTMENT
Bibliographic record
Abstract
Background and aims: Triage systems are widely applied in emergency departments to prioritize patients. A systematic appraisal of the evidence regarding their performance in children is lacking. The aim of this study was to assess the performance of triage systems for identifying high and low urgency children in the emergency department Methods: We systematically searched five electronic databases from 1980 to 2016. Studies that evaluated an emergency medical triage system, assessed validity using any reference standard as proxy for true patient urgency and were written in English were included. Reviewers identified studies, extracted data, and assessed quality of evidence independently and in duplicate. Raw data were extracted to create 2x2 tables and calculate sensitivity and specificity. ED patient volume and case-mix were investigated as determinants of triage systemsu2019 performance.Results : Twenty-five eligible studies were conducted in children evaluating nine different triage systems. Only three triage systems had more than one evaluation: Canadian Triage and Acuity Scale, Emergency Severity Index, and Manchester Triage System. A wide range of reference standards was used. Although the number of studies was low, overall validity to identify high and low urgency patients appeared moderate to good, but performance was highly variable. For hospitalisation, the most commonly reported outcome, sensitivity ranged from 0.13 to 0.85 and specificity from 0.70 to 0.96. No clear association was found between ED patient volume or case-mix and triage systemsu2019 performance. Conclusions: Established triage systems show a reasonable validity for the triage of patients at the ED, but performance varies considerably. Important research questions that remain are what determinants influence triage systemsu2019 performance and how the performance of existing triage systems can be improved.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".