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Record W2257506122 · doi:10.1016/j.ijsu.2016.01.086

Cervical spine evaluation in the bluntly injured patient

2016· review· en· W2257506122 on OpenAlexaboutno aff
Tanya L. Zakrison, Brian H. Williams

Bibliographic record

VenueInternational Journal of Surgery · 2016
Typereview
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBlunt traumaMagnetic resonance imagingCervical spineBluntRadiologyCervical spine injurySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Cervical spine injuries causing spinal cord trauma are rare in blunt trauma yet lead to devastating morbidity and mortality when they occur. There exists considerable debate in the literature about the best way for clinicians to proceed in ruling out cervical spine injuries in alert or obtunded blunt trauma patients. METHODS: We reviewed the current literature and practice management guidelines to generate clinical recommendations for the detection and clearance of cervical spine injuries in the blunt trauma patient. RESULTS: The NEXUS and Canadian C-Spine Rules are clinical tools to guide in the clearance of the cervical spine of patients who have sustained low risk trauma and who are pain free, with the Canadian C-Spine Rules having superior sensitivity and specificity. In the alert, high risk patient with pain (or without, if over the age of 65 years), follow up imaging is required. The best imaging modality to use is Computerized Tomography (CT) of the cervical spine. In the obtunded trauma patient, CT clearance of c-spine injury is adequate, unless there is soft tissue injury or any non-bony abnormalities detected. At such point, definitive clearance may be obtained with Magnetic Resonance Imaging (MRI). CONCLUSIONS: It is imperative to assume cervical spine injury in the blunt trauma patient. Clinical decision rules for cervical clearance may be used in low risk patients, avoiding imaging. High risk patients require imaging in the form of CT scan of the cervical spine.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.110
GPT teacher head0.432
Teacher spread0.322 · 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
GenreReview

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

Citations8
Published2016
Admission routes1
Has abstractyes

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