(A279) Evidence-Based Decision-Making in Triage
Bibliographic record
Abstract
Background and Aims Decision-making is the major component in triaging emergency department patients. Influencing factors on decision-making have been identified but it`s not clear how much of the decision is based upon scientific criteria. The objective of this study was to determine frequency of using reliable and valid guidelines by nurses in emergency departments. Methods It was a descriptive survey study. The questionnaire was composed of demographic data, evidence-based triage questions (15) and triage decision-making questions (10). The questionnaire reliability was 0.87 using the test-retest method. Content validity was considered based upon Canadian Triage and Acuity Scale. Results 70 nurses from 10 emergency departments participated. 40 % of nurses` responses to evidence-based questions was correct. The percentage of inter-rater agreement between nurses was moderate (0.56) related to decision-making questions. No valid and reliable guideline was utilized in emergency departments. Conclusion Nurses` decision-making was poorly based on evidence-based criteria. Low level of nurses` knowledge about triage may be derived from lack of official and specialized triage training courses. Academic triage courses establishment and development of national triage scale are recommended.
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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 | 0.028 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".