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Record W2155363577 · doi:10.1089/tmj.2008.0076

Application of Tele-Ultrasound in Emergency Medical Services

2008· article· en· W2155363577 on OpenAlexaff
Mei-Ju Su, Matthew Huei‐Ming, Chow-In Ko, Wen‐Chu Chiang, Chih‐Wei Yang, Sao‐Jie Chen, Robert HC Chen, Heng‐Shuen Chen

Bibliographic record

VenueTelemedicine Journal and e-Health · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTechnicianMedical emergencyMedicineEmergency medical servicesEmergency departmentEmergency physicianEmergency medicineNursing

Abstract

fetched live from OpenAlex

In emergency medical services, portable ultrasound scanners have the potential to become new-age stethoscopes for emergency physicians. For trauma cases in particular, portable ultrasound scanners can scan the chest and abdomen of emergency patients both rapidly and conveniently. This study describes the development of tele-ultrasound for pre-diagnosis in a medical emergency setting as a part of the updated Mobile Hospital Emergency Medical System (MHEMS). An emergency medical technician can provide an emergency physician with a patient's ultrasound images and medical information during the patient's pre-hospitalization and transportation period using a combination of the MHEMS, the portable ultrasound scanner, and the onboard 3G communication capabilities. The MHEMS includes a Dispatch and Mission Control Center that facilitates the communication between the Emergency Department of a specified hospital, the systems aboard the ambulance. Early receipt of information relevant to the patient will enhance pre-diagnosis options for on-duty emergency physicians and allow for a hospital's emergency department to promptly prepare necessary surgical instruments or beds. Furthermore, emergency medical technicians can also obtain instructions from on-duty physicians to enhance damage and disaster control ability in critical moments.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.278

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)0.0000.000

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 head0.016
GPT teacher head0.326
Teacher spread0.310 · 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

Citations49
Published2008
Admission routes1
Has abstractyes

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