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Record W2513116955 · doi:10.1097/ta.0000000000001187

A novel methodology to characterize interfacility transfer strategies in a trauma transfer network

2016· article· en· W2513116955 on OpenAlexafffundabout
David Gómez, Barbara Haas, Kristian Larsen, Aziz S. Alali, Russell D. MacDonald, Jeffrey M. Singh, Homer Tien, Theodore J. Iwashyna, Gordon D. Rubenfeld, Avery B. Nathens

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2016
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsInterquartile rangeTransfer (computing)MedicineTransfer stationEmergency medicineMedical emergencyComputer scienceSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: More than half of severely injured patients are initially transported from the scene of injury to nontrauma centers (NTCs), with many requiring subsequent transfer to trauma center (TC) care. Definitive care in the setting of severe injury is time sensitive. However, transferring severely injured patients from an NTC is a complex process often fraught with delays. Selection of the receiving TC and the mode of interfacility transport both strongly influence total transfer time and are highly amenable to quality improvement initiatives. METHODS: We analyzed transfer strategies, defined as the pairing of a destination and mode of transport (land vs. rotary wing vs. fixed wing), for severely injured adult patients. Existing transfer strategies at each NTC were derived from trauma registry data. Geographic Information Systems network analysis was used to identify the strategy that minimized transfer times the most as well as alternate strategies (+15 or +30 minutes) for each NTC. Transfer network efficiency was characterized based on optimality and stability. RESULTS: We identified 7,702 severely injured adult patients transferred from 146 NTCs to 9 TCs. Nontrauma centers transferred severely injured patients to a median of 3 (interquartile range, 1-4) different TCs and utilized a median of 4 (interquartile range, 2-6) different transfer strategies. After allowing for the use of alternate transfer strategies, 73.1% of severely injured patients were transported using optimal/alternate strategies, and only 40.4% of NTCs transferred more than 90% of patients using an optimal/alternate transfer strategy. Three quarters (75.5%) of transfers occurred between NTCs and their most common receiving TC. CONCLUSION: More than a quarter of patients with severe traumatic injuries undergoing interfacility transport to a TC in Ontario are consistently transported using a nonoptimal combination of destination and mode of transport. Our novel analytic approach can be easily adapted to different system configurations and provides actionable data that can be provided to NTCs and other stakeholders. LEVEL OF EVIDENCE: Therapeutic study, level IV.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.082
GPT teacher head0.361
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2016
Admission routes3
Has abstractyes

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