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Methodology for the Formulation of the Guidelines for the Management of Moderate to Severe Traumatic Brain Injury in Austere and Combat Environments

2024· article· en· W4404349227 on OpenAlexaff
Ross C. Puffer, Andrés M. Rubiano, Simon Oczkowski, Gregory W. J. Hawryluk, Jamshid Ghajar, Halinder S. Mangat, Randy S. Bell, Jeffrey V. Rosenfeld, Lynne Lourdes N. Lucena, William R. Copeland, Grant W. Mallory, Scott A. Cota, Bradley A. Dengler

Bibliographic record

VenueNeurosurgery Open · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsMcMaster UniversityImpact
FundersUniformed Services University of the Health SciencesU.S. Department of Defense
KeywordsNeurointensive careTraumatic brain injuryMedicineGuidelineBest practiceMedical emergencyTertiary careLimited resourcesIntensive care medicineEmergency medicineRisk analysis (engineering)Psychiatry

Abstract

fetched live from OpenAlex

Care for the patient with traumatic brain injury (TBI) in austere or combat environments is challenging because resources are substantially limited as compared with care for these patients in a tertiary medical facility. Significant research has been and will continue to be performed on TBI care in these settings. This includes high-quality, evidence-based guidelines that are routinely updated to help guide the treating team as to best practices for a wide range of TBI presentations, complications, and outcomes. Much less is known regarding best practices for TBI care in a resource-limited environment, such as a facility in an austere environment without advanced imaging, dedicated neurointensive care, or definitive neurosurgical capabilities. The aim of this study was to identify the methodology that will be used for an upcoming in-person guideline conference, focusing on the care of patients with TBI in resource-limited austere and/or combat zones.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.247
GPT teacher head0.412
Teacher spread0.166 · 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 designOther design
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

Citations0
Published2024
Admission routes1
Has abstractyes

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