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Difference in trauma team activation criteria between hospitals within the same region

2005· article· en· W2058052503 on OpenAlexaff
Jason Smith, Erica Caldwell, Michael Sugrue

Bibliographic record

VenueEmergency Medicine Australasia · 2005
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsMichael Smith Health Research BC
Fundersnot available
KeywordsMedicineTriageEmergency medicineMetropolitan areaMedical emergencyAudit

Abstract

fetched live from OpenAlex

OBJECTIVES: The present study was conducted to establish the current criteria for trauma team activation (TTA) in hospitals in the Metropolitan Sydney area, and examine the rationale behind their use. METHODS: A cross-sectional survey was undertaken of the seven hospitals in the Metropolitan Sydney area designated to receive adult major trauma in March 2004. Trauma coordinators in each hospital provided the criteria used for adult TTA within their hospital. RESULTS: All seven hospitals replied with their TTA criteria and completed the survey. The results show a wide variation in those criteria used by hospitals to activate their trauma team. Universally used criteria included penetrating injury to the head, neck or torso, limb amputation, spinal cord injury and systolic blood pressure <90 mmHg. Physiological limits for TTA varied between hospitals, with different limits for pulse rate and GCS used in different hospitals. All hospitals used mechanism of injury criteria alone as an activation prompt. CONCLUSIONS: The criteria for TTA differ between hospitals within the same region. The criteria currently used will result in over-triage of trauma patients, but this might be of benefit in training the trauma team in centres that do not see a large volume of trauma patients. There are several advantages in standardization of criteria including optimization of patient care, training, research and audit. Further work is needed to validate existing criteria for use throughout the region.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.351
Teacher spread0.290 · 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

Citations23
Published2005
Admission routes1
Has abstractyes

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