원저 : 변형된 포괄적 5단계 중증도 분류도구(modified Canadian Triage and Acuity Scale)의 신뢰도 분석
Bibliographic record
Abstract
Purpose: Congestion at emergency department (ED) entrances is a common problem that makes it difficult to assign patients proper priority for treatment based on the severity of their condition. A comprehensive five-scale triage system has widely been accepted over other scaling systems as the most effective triage tool in ED. Our aim was to evaluate the reliability and usefulness of the modified Canadian Triage and Acuity Scale (mCTAS) as a comprehensive five-scale triage system. Methods: Two hundred ninety-eight ED patients were recruited in the triage room of one university hospital between December 7 and December 24, 2004. A modified mCTAS was tested for inter-rater reliability by emergency physicians and clinical nurse specialists (CNSs). mCTAS and the Modified Asan Triage Score (MATS) were calculated by doctors and nurses independently. Clinical values measured were discharge decisions, admission to general ward or intensive care unit, and time to clinical decisions. SAS and SPSS 10.0 for Windows were used for statistical analysis. Results: The weighted kappa statistics were 0.65 among the CNSs (95% CI = 0.47-0.84) and 0.67 for emergency physicians (95% CI = 0.58-0.77). respectively. As evaluated by mCTAS, The ICU admission rate was higher for level 1 cases (40.00%) and level 2 cases (27.5%) than for other levels (p < 0.01). mCTAS and MATS scores indicated that more severe cases had a higher rate of admission to the ICU and the less severity cases showed a higher rate of discharge from the ED. In addition, t-value and F value scores revealed that the sensitivity of mCTAS (t = - 6.30, F= - 39.63) was greater than that of MATS (t = -5.48, F =30.02). Conclusion: mCTAS showed good inter-rater reliability and usefulness as a triage tool and was more reliable than MAST in indicating the severity of condition of ED patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".