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Record W2055106002 · doi:10.1097/ta.0b013e31824baddf

Impact of prehospital mode of transport after severe injury

2012· article· en· W2055106002 on OpenAlexfundno aff
Eileen M. Bulger, Danielle Guffey, Francis X. Guyette, Russell D. MacDonald, Karen J. Brasel, Jeffery D. Kerby, Joseph P. Minei, Craig R. Warden, Sandro Rizoli, Laurie J. Morrison, Graham Nichol

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2012
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsMedicineGlasgow Coma ScaleInjury Severity ScoreTraumatic brain injuryBlood pressureGlasgow Outcome ScaleAnesthesiaEmergency medicineHeart ratePoison controlInternal medicineInjury prevention

Abstract

fetched live from OpenAlex

BACKGROUND: There is ongoing controversy about the relative effectiveness of air medical versus ground transportation for severely injured patients. In some systems, air medical crews may provide a higher level of care but may require longer transport times. We sought to evaluate the impact of mode of transport on outcome based on analysis of data from two randomized trials of prehospital hypertonic resuscitation. METHODS: Injured patients were enrolled based on prehospital evidence of hypovolemic shock (systolic blood pressure ≤70 mm Hg or systolic blood pressure = 71-90 mm Hg with heart rate ≥108 bpm) or severe traumatic brain injury (TBI; Glasgow Coma Scale score ≤8). Patient demographics, injury severity, and physiology were compared based on mode of transport. Multivariate logistic regression was used to determine the impact of mode of transport on 24-hour and 28-day survival for all patients and 6-month extended Glasgow Outcome Scale for patients with TBI, adjusting for differences in injury severity. RESULTS: Included were 2,049 patients, of which 703 (34%) were transported by air. Patients transported by air were more severely injured (mean Injury Severity Score, 30.3 vs. 22.8; p < 0.001), more likely to be in the TBI cohort (70% vs. 55.4%; p < 0.001), and more likely blunt mechanism (94.0% vs. 78.1%; p < 0.001). Patients transported by air had higher rates of prehospital intubation (81% vs. 36%; p < 0.001), received more intravenous fluids (mean 1.3 L vs. 0.8 L; p < 0.001), and had longer prehospital times (mean 76.1 minutes vs. 43.5 minutes; p < 0.001). Adjusted analysis revealed no significant impact of mode of transport on survival or 6-month neurologic outcome (air transport-28-day survival: odds ratio, 1.11; 95% confidence interval, 0.82-1.51; 6-month extended Glasgow Outcome Scale score ≤4: odds ratio, 0.94; 95% confidence interval, 0.68-1.31). CONCLUSION: There was no difference in the adjusted clinical outcome according to mode of transport. However, air medical transported more severely injured patients with more advanced life support procedures and longer prehospital time. LEVEL OF EVIDENCE: III.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.339
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), 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

Citations83
Published2012
Admission routes1
Has abstractyes

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