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Record W1999441655 · doi:10.1136/jramc-2014-000350

A survey of major trauma centre staffing in England

2015· article· en· W1999441655 on OpenAlexfundno aff
Jan O. Jansen, Jonathan J. Morrison, Nigel Tai, Mark J. Midwinter

Bibliographic record

VenueJournal of the Royal Army Medical Corps · 2015
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
FundersUniversity of AberdeenMcMaster University
KeywordsStaffingMedicineMajor traumaService delivery frameworkService (business)Medical emergencyMultidisciplinary approachNursingBusiness

Abstract

fetched live from OpenAlex

INTRODUCTION: Trauma care delivery in England has been transformed by the development of trauma networks, and the designation of trauma centres. A specialist trauma service is a key component of such centres. The aim of this survey was to determine to which extent, and how, the new major trauma centres (MTCs) have been able to implement such services. METHODS: Electronic questionnaire survey of MTCs in England. RESULTS: All 22 MTCs submitted responses. Thirteen centres have a dedicated major trauma service or trauma surgery service, and a further four are currently developing such a service. In 7 of these 17 centres, the service is or will be provided by orthopaedic surgeons, in 2 by emergency medicine departments, in another 2 by general or vascular surgeons, and in 6 by a multidisciplinary group of consultants. DISCUSSION: A large proportion of MTCs still do not have a dedicated major trauma service. Furthermore, the models which are emerging differ from other countries. The relative lack of involvement of surgeons in MTC trauma service provision is particularly noteworthy, and a potential concern. The impact of these different models of service delivery is not known, and warrants further study.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.055
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 integrity0.0000.001
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 head0.048
GPT teacher head0.297
Teacher spread0.248 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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