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Record W2891328515 · doi:10.1093/milmed/usy117

Emergency Resuscitative Thoracotomy in the Combat or Operational Environment

2018· article· en· W2891328515 on OpenAlexaff
T. Monchal, Matthew J. Martin, Jared L. Antevil, Donald R. Bennett, William C. DeVries, Scott Zakaluzny, Robert L. Ricca, Homer Tien, Philip S. Mullenix, Zsolt T. Stockinger

Bibliographic record

VenueMilitary Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicTrauma Management and Diagnosis
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsMedicineThoracotomyBluntPenetrating traumaVital signsResuscitationEmergency medicineMortality rateBlunt traumaSurvival rateAnesthesiaIntensive care medicineMedical emergencySurgery

Abstract

fetched live from OpenAlex

Resuscitative thoracotomy has been extensively described in the civilian trauma literature and has a high mortality rate, due largely to the nature of the injuries leading to arrest. The survival rates are generally highest (10-30%) for penetrating truncal injuries and patients who arrive with vital signs and proceed to arrest or who have impending arrest. They are significantly lower (less than 5%) for blunt trauma victims, particularly those who arrest in the field or during transport (1% or less). In addition, the likelihood of survival with intact neurologic function is significantly lower than the overall survival rates, particularly for blunt trauma victims and for prehospital arrest.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.993

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)0.0080.000

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 head0.059
GPT teacher head0.342
Teacher spread0.283 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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