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Record W2942662500 · doi:10.1055/s-0035-1554199

AOSpine Subaxial Cervical Spine Injury Classification System

2015· article· en· W2942662500 on OpenAlexaff
Alexander R. Vaccaro, Christopher K. Kepler, John D. Koerner, Marcel F. Dvorak, Jens R. Chapman, Michael G. Fehlings, Bizhan Aarabi, Shanmuganathan Rajasekaran, Gregory D. Schroeder, Frank Kandziora, Klaus John Schnake, Carlo Bellabarba, Luiz Roberto Vialle, Maximilian Reinhold, F. Cumhur Öner

Bibliographic record

VenueGlobal Spine Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsMedicineCervical spine injuryCervical spineReliability (semiconductor)Physical therapyKappaGrading (engineering)RadiologyPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

Introduction The purpose of this project is to describe a morphology-based subaxial cervical traumatic injury classification system. Similar to the effort put toward the thoracolumbar system, the goal was to develop a comprehensive yet simple classification system with high intra and interobserver reliability to be used for clinical and research purposes. Material and Methods A subaxial cervical spine injury classification system was developed using a consensus process. All investigators were required to successfully grade 10 cases to demonstrate comprehension of the system before grading 30 additional cases on two occasions, 1 month apart. Kappa coefficients ( κ) were calculated for interobserver and intraobserver reliability. Results The classification system is based on the following three injury types: compression injuries (A), tension band injuries (B), and translational injuries (C), with additional descriptions for facet injuries, as well as patient-specific modifiers and neurologic status. Interobserver and intraobserver reliability was substantial for all injury subtypes ( κ = 0.64 and 0.75, respectively). Conclusion The AOSpine subaxial cervical spine injury classification system demonstrated substantial reliability in this initial assessment, and could be a valuable tool for patient care and for research purposes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.686

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)0.0000.000

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 head0.035
GPT teacher head0.340
Teacher spread0.305 · 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 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

Citations9
Published2015
Admission routes1
Has abstractyes

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