MétaCan
Menu
Back to cohort

Comparing Methodologies for Evaluating Emergency Medical Services Ground Transport Access to Time‐critical Emergency Services: A Case Study Using Trauma Center Care

2012· article· en· W1528367856 on OpenAlexafffundabout
Aristithes G. Doumouras, David Gómez, Barbara Haas, Donald M. Boyes, Avery B. Nathens

Bibliographic record

VenueAcademic Emergency Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsTriageEmergency medical servicesMedicineTrauma centerMedical emergencyPopulationCatchment areaEmergency medicineGeographyEnvironmental healthCartographyRetrospective cohort studySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: The regionalization of medical services has resulted in improved outcomes and greater compliance with existing guidelines. For certain "time-critical" conditions intimately associated with emergency medicine, early intervention has demonstrated mortality benefits. For these conditions, then, appropriate triage within a regionalized system at first diagnosis is paramount, ideally occurring in the field by emergency medical services (EMS) personnel. Therefore, EMS ground transport access is an important metric in the ongoing evaluation of a regionalized care system for time-critical emergency services. To our knowledge, no studies have demonstrated how methodologies for calculating EMS ground transport access differ in their estimates of access over the same study area for the same resource. This study uses two methodologies to calculate EMS ground transport access to trauma center care in a single study area to explore their manifestations and critically evaluate the differences between the methodologies. METHODS: Two methodologies were compared in their estimations of EMS ground transport access to trauma center care: a routing methodology (RM) and an as-the-crow-flies methodology (ACFM). These methodologies were adaptations of the only two methodologies that had been previously used in the literature to calculate EMS ground transport access to time-critical emergency services across the United States. The RM and ACFM were applied to the nine Level I and Level II trauma centers within the province of Ontario by creating trauma center catchment areas at 30, 45, 60, and 120 minutes and calculating the population and area encompassed by the catchments. Because the methodologies were identical for measuring air access, this study looks specifically at EMS ground transport access. RESULTS: Catchments for the province were created for each methodology at each time interval, and their populations and areas were significantly different at all time periods. Specifically, the RM calculated significantly larger populations at every time interval while the ACFM calculated larger catchment area sizes. This trend is counterintuitive (i.e., larger catchment should mean higher populations), and it was found to be most disparate at the shortest time intervals (under 60 minutes). Through critical evaluation of the differences, the authors elucidated that the ACFM could calculate road access in areas with no roads and overestimates access in low-density areas compared to the RM, potentially affecting delivery of care decisions. CONCLUSIONS: Based on these results, the authors believe that future methodologies for calculating EMS ground transport access must incorporate a continuous and valid route through the road network as well as use travel speeds appropriate to the road segments traveled; alternatively, we feel that variation in methods for calculating road distances would have little effect on realized access. Overall, as more complex models for calculating EMS ground transport access become used, there needs to be a standard methodology to improve and to compare it to. Based on these findings, the authors believe that this should be the RM.

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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.377
GPT teacher head0.540
Teacher spread0.163 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueAcademic Emergency MedicineSame topicTrauma and Emergency Care StudiesFrench-language works237,207