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Record W2321415763 · doi:10.1136/bcr-2014-204890

Unstable C-spine injury with normal C-spine radiographs

2014· article· en· W2321415763 on OpenAlexaboutno aff
M. Razaq, Terasa Broom

Bibliographic record

VenueBMJ Case Reports · 2014
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRadiographyCervical spineBlunt traumaRadiologyNexus (standard)SPINE (molecular biology)Cervical spine injurySurgery

Abstract

fetched live from OpenAlex

There is some controversy surrounding the optimal mode of imaging in trauma patients with suspected cervical (C) spine injury. Various rules (most notably the Canadian C-spine rules and the NEXUS rules) have been designed to help reduce the need for imaging given the poor yield. Some authorities advocate CT for almost all cases whereas others advocate three view radiographs unless the patient is at high risk, in which case CT is the preferred choice. One meta-analysis showed sensitivity of 58% (39-76%) for plain radiographs and 98% for CT in identification of C-spine injuries following blunt trauma. This case report illustrates how very unstable C-spine injuries may not be apparent on plain radiographs and a degree of clinical suspicion may be required for further imaging.

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)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.010
GPT teacher head0.296
Teacher spread0.286 · 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 designCase report
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

Citations1
Published2014
Admission routes1
Has abstractyes

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