(A260) Triage Decision-Making in Intoxication
Bibliographic record
Abstract
Background and Aims Decision-making is the major component in triaging EDs patients. EDs Triage systems have applied different approaches to triaging intoxicated patients. Pros & Cons for these approaches need to be identified. Aim is to analysis management of intoxicated patients during various triage process. Methods Critical review includes five triage systems, Emergency Severity Index, Australasian Triage Scale, Canadian triage and Acuity Scale, Manchester Triage System and 5-tier Triage protocol. These systems have been analyzed via meta-synthesis in terms of evidence-based criteria, inclusiveness, specific application and practicability. Results General physiologic signs & symptoms were the gold standard for determining acuity in patients that have been applied by all triage systems. Conscious level, air way, respiratory status and circulation assessment were identified as major criteria in decision-making. 5-tier Triage protocol showed the most comprehensiveness characteristics to prioritizing intoxicated patients. Discussion Resources necessary for evidence-based performance to support nursing decisions in triaging intoxicated patients needs fundamentally to be developed. It`s necessary to develop National Triage Scale to approach intoxicated patients effectively.
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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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".