Triage effect of three emergency pre⁃examination triage standard:a Meta⁃analysis
Bibliographic record
Abstract
Abstract [Objective]To evaluate triage effects of Manchester Triage Scale(MTS),Emergency Severity Index(ESI),Canadian Triage and Acuity Scale(CTAS).[Methods]PubMed,the Cochrance Library,EMbase,Web of Science,Chinese Biomedical Literature Database,VIP Database,Wanfang Database,CNKI were systematically searched.And researches on triage effects of emergency pre⁃examination triage standards,like MTS,ESI and CTAS were collected.The retrieval time was from inception to August 31,2019.Literatures screening and data extraction were carried out by two researchers,independently.Sensitivity,specificity and diagnostic odds ratio(DOR) of triage effect of emergency pre⁃examination triage standards were combined by adpoting bivariate mixed effects model.Sensitivity and specificity were used to construct receiver operating characteristic curve(ROC).And the area under ROC curve(AUC)was used to evaluate triage effect of each emergency pre⁃examination triage standard.Deek funnel was used to evaluate publication bias.Fan Gen chart was used to evaluate clinical applicability of triage standards.[Results]A total of 35 studies were included.Overall Meta analysis results showed that combined sensitivity of MTS was 0.57,95%(0.49,0.65),=0.00.Specificity of MTS was 0.84,95%(0.80,0.87),=0.00.DOR was 6.94,95%(5.08,9.48),=0.00.AUC was 0.80,95%(0.76,0.83).The combined sensitivity of ESI was 0.50,95%(0.38,0.62),=0.00.Specificity was 0.87,95%(0.82,0.91),=0.00.DOR was 6.84,95%(5.21,8.98),=0.00.AUC was 0.80,95%(0.77,0.84).The combined sensitivity of CTAS was 0.35,95%(0.29,0.43),=0.00.Its specificity was 0.92,95%(0.88,0.94),=0.00.DOR was 5.98,95%(4.31,8.29),=0.00.AUC was 0.72,95%(0.68,0.76).Sensitivity analysis verified that research results were relatively stable.There was no obvious bias in each study.There were obvious heterogeneity in three emergency pre⁃examination triage standards.Subgroup analysis showed that populations were main source of heterogeneity among three emergency pre⁃examination triage standards.The overall analysis of Fan Gen chart showed that probability of subjects need to be hospitalized would increase when triage criteria were positive in three triage standards.[Conclusions]Existing evidence showed that the overall accuracy of MTS and ESI could be better in pre⁃examination triage of emergency patients,but diagnostic efficiency of both way needed to be improved furtherly.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".