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Record W4291163888 · doi:10.1016/j.spinee.2022.08.005

AO Spine upper cervical injury classification system: a description and reliability study

2022· article· en· W4291163888 on OpenAlexaff
Alexander R. Vaccaro, Mark J. Lambrechts, Brian A. Karamian, José A. Canseco, F. Cumhur Öner, Emiliano Vialle, Shanmuganathan Rajasekaran, Lorin M. Benneker, Frank Kandziora, Mohammad El‐Sharkawi, Jin W. Tee, Richard J. Bransford, Andrei Fernandes Joaquim, Sander Muijs, Martin Holas, Masahiko Takahata, Waeel Hamouda, Rishi Mugesh Kanna, Klaus John Schnake, Christopher K. Kepler, Gregory D. Schroeder

Bibliographic record

VenueThe Spine Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsUniversity of British Columbia
FundersAO FoundationMinistry of Education, Culture, Sports, Science and Technology
KeywordsMedicineCervical spine injuryCervical spineKappaReproducibilityReliability (semiconductor)Cohen's kappaPhysical therapyRadiologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND CONTEXT: Prior upper cervical spine injury classification systems have focused on injuries to the craniocervical junction (CCJ), atlas, and dens independently. However, no previous system has classified upper cervical spine injuries using a comprehensive system incorporating all injuries from the occiput to the C2-3 joint. PURPOSE: To (1) determine the accuracy of experts at correctly classifying upper cervical spine injuries based on the recently proposed AO Spine Upper Cervical Injury Classification System (2) to determine their interobserver reliability and (3) identify the intraobserver reproducibility of the experts. STUDY DESIGN/SETTING: International Multi-Center Survey. PATIENT SAMPLE: A survey of international spine surgeons on 29 unique upper cervical spine injuries. OUTCOME MEASURES: Classification accuracy, interobserver reliability, intraobserver reproducibility. METHODS: Thirteen international AO Spine Knowledge Forum Trauma members participated in two live webinar-based classifications of 29 upper cervical spine injuries presented in random order, four weeks apart. Percent agreement with the gold-standard and kappa coefficients (ƙ) were calculated to determine the interobserver reliability and intraobserver reproducibility. RESULTS: Raters demonstrated 80.8% and 82.7% accuracy with identification of the injury classification (combined location and type) on the first and second assessment, respectively. Injury classification intraobserver reproducibility was excellent (mean, [range] ƙ=0.82 [0.58-1.00]). Excellent interobserver reliability was found for injury location (ƙ = 0.922 and ƙ=0.912) on both assessments, while injury type was substantial (ƙ=0.689 and 0.699) on both assessments. This correlated to a substantial overall interobserver reliability (ƙ=0.729 and 0.732). CONCLUSIONS: Early phase validation demonstrated classification of upper cervical spine injuries using the AO Spine Upper Cervical Injury Classification System to be accurate, reliable, and reproducible. Greater than 80% accuracy was detected for injury classification. The intraobserver reproducibility was excellent, while the interobserver reliability was substantial.

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)0.000
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 score0.651
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.296
Teacher spread0.269 · 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

Citations29
Published2022
Admission routes1
Has abstractyes

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