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Record W2802021139 · doi:10.1017/s1049023x1800033x

Comparison of Electronic Versus Manual Mass-Casualty Incident Triage

2018· article· en· W2802021139 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePrehospital and Disaster Medicine · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsTriageMass-casualty incidentMedical emergencyMedicineObservational studyHealth careDisaster medicineEmergency medicinePoison controlHuman factors and ergonomics

Abstract

fetched live from OpenAlex

IntroductionMass-casualty incidents (MCIs) easily overwhelm a health care facility's human and material resources through the extraordinary influx of casualties. Efficient and accurate triage of incoming casualties is a critical step in the hospital disaster response.Hypothesis/ProblemTraditionally, triage during MCIs has been manually performed using paper cards. This study investigated the use of electronic Simple Triage and Rapid Treatment (START) triage as compared to the manual method. METHODS: This observational, crossover study was performed during a live MCI simulation at an urban, Canadian, Level 1 trauma center on May 26, 2016. Health care providers (two medical doctors [MDs], two paramedics [PMs], and two registered nurses [RNs]) each triaged a total of 30 simulated patients - 15 by manual (paper-based) and 15 by electronic (computer-based) START triage. Accuracy of triage categories and time of triage were analyzed. Post-simulation, patients and participating health care providers also completed a feedback form. RESULTS: There was no difference in accuracy of triage between the electronic and manual methods overall, 83% and 80% (P=1.0), between providers or between triage categories. On average, triage time using the manual method was estimated to be 8.4 seconds faster (P<.001) for PMs; and while small differences in triage times were observed for MDs and RNs, they were not significant. Data from the participant feedback survey showed that the electronic method was preferred by most health care providers. Patients had no preference for either method. However, patients perceived the computer-based method as "less personal" than the manual triage method, but they also perceived the former as "better organized." CONCLUSION: Hospital-based electronic START triage had the same accuracy as hospital-based manual START triage, regardless of triage provider type or acuity of patient presentations. Time of triage results suggest that speed may be related to provider familiarity with a modality rather than the modality itself. Finally, according to patient and provider perceptions, electronic triage is a feasible modality for hospital triage of mass casualties. Further studies are required to assess the performance of electronic hospital triage, in the context of a rapid surge of patients, and should consider additional efficiencies built in to electronic triage systems. This study presents a framework for assessing the accuracy, triage time, and feasibility of digital technologies in live simulation training or actual MCIs. BolducC, MaghrabyN, FokP, LuongTM, HomierV. Comparison of electronic versus manual mass-casualty incident triage. Prehosp Disaster Med. 2018;33(3):273-278.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.085
GPT teacher head0.488
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it