Effects of a Triage Process Conversion on the Triage of High‐risk Presentations
Bibliographic record
Abstract
OBJECTIVES: The objective was to determine effects of a modification in triage process on triage acuity distribution in general and among patients with conditions requiring time-sensitive therapy. METHODS: The authors retrospectively reviewed triage acuity distributions before and after modification of their triage process that entailed conversion from the Canadian Triage and Acuity Scale (CTAS) to the Emergency Severity Index (ESI). The authors calculated the ratio of the odds of being triaged to a nonemergent level (3, 4, or 5) under ESI to the odds of being triaged as nonemergent under CTAS. The authors calculated sensitivity and specificity of triage to an emergent acuity level (1 or 2) for identifying patients with common presentations who required time-sensitive care. RESULTS: There were shifts from higher to lower acuity levels for all subsets, with odds ratios ranging from 2.80 (95% confidence interval [CI] = 2.75 to 2.86) for all patients to 21.39 (95% CI = 14.66 to 31.21) for patients over 55 years of age with a chief complaint of chest pain. The sensitivity of triage for identifying abdominal pain patients requiring admission to an intensive care unit (ICU) or operating room (OR) or emergency department (ED) death was 80.7% (95% CI = 73.2 to 86.5) before versus 50.8% (95% CI = 43.5 to 58.1) following the transition to ESI. Specificity under CTAS, 55.2% (95% CI = 54.0 to 56.4), was significantly lower than under ESI, 83.6% (95% CI = 82.7 to 84.4). The authors found similar effects for patients presenting with chest pain. CONCLUSIONS: Monitoring for changes in the sensitivity of the triage process for detecting patients with potentially time-sensitive conditions should be considered when modifying triage processes. Further work should be done to determine if the decreased sensitivity seen in this study occurs in other institutions converting to ESI, and potential causative factors should be explored.
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 | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".