Review: Some ED triage systems better predict ED mortality than in-hospital mortality or hospitalization
Bibliographic record
Abstract
ACP Journal Club16 April 2019Review: Some ED triage systems better predict ED mortality than in-hospital mortality or hospitalizationThwe Htay, MD, KoKo Aung, MD, MPHThwe Htay, MDPaul L. Foster School of Medicine, El Paso, Texas, USA (T.H., K.A.)Search for more papers by this author, KoKo Aung, MD, MPHPaul L. Foster School of Medicine, El Paso, Texas, USA (T.H., K.A.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJ201904160-047 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Source CitationHinson JS, Martinez DA, Cabral S, et al. Triage performance in emergency medicine: a systematic review. Ann Emerg Med. 2018. [Epub ahead of print]. https://pubmed.ncbi.nlm.nih.gov/30470513Clinical Impact RatingsEmergency Med: References1 Nakao H, Ukai I, Kotani J. A review of the history of the origin of triage from a disaster medicine perspective. Acute Med Surg. 2017;4:379-84. [PMID: 29123897] Google Scholar2 Bullard MJ, Melady D, Emond M, et al. Guidance when applying the Canadian Triage and Acuity Scale (CTAS) to the geriatric patient: executive summary. CJEM. 2017;19:S28-S37. [PMID: 28756798] Google Scholar Author, Article, and Disclosure InformationAffiliations: Paul L. Foster School of Medicine, El Paso, Texas, USA (T.H., K.A.)This article was published at Annals.org on 2 April 2019. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byAn external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency departmentDevelopment and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions 16 April 2019Volume 170, Issue 8Page: JC47KeywordsCohort studiesEmergency departmentHospitalizationsMortalityPopulation statisticsPulmonary embolismSepsisSystematic reviewsTriageVital signs ePublished: 16 April 2019 Issue Published: 16 April 2019 Copyright & PermissionsCopyright © 2019 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.009 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".