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Record W4407622395 · doi:10.1097/brs.0000000000005297

Validating the Hierarchical Nature of the AO Spine Upper Cervical Spine Injury Classification System

2025· article· en· W4407622395 on OpenAlexaff
Rajkishen Narayanan, Jonathan Dalton, Richard J. Bransford, Harvinder Singh Chhabra, Andrei Fernandes Joaquim, Mohammad El‐Sharkawi, Lorin M. Benneker, Klaus John Schnake, F. Cumhur Öner, Charlotte Dandurand, José A. Canseco, Christopher K. Kepler, Alexander R. Vaccaro, Gregory D. Schroeder

Bibliographic record

VenueSpine · 2025
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineSpinal cord injuryCervical spineNeurosurgeryNonunionInjury Severity ScorePhysical therapyInjury preventionPoison controlPhysical medicine and rehabilitationSurgerySpinal cordEmergency medicine

Abstract

fetched live from OpenAlex

STUDY DESIGN: Global cross-sectional survey. OBJECTIVE: To validate the hierarchical nature of the AO Spine Upper Cervical Spine Injury Classification (UCIC) across AO geographical regions/practice experience. SUMMARY OF BACKGROUND DATA: To create a universally validated scheme with prognostic value, AO Spine established an upper cervical spine injury classification involving three elements: injury morphology (region: I-occipital condyle and craniocervical junction; II-C1 ring and C1-2 joint; III-C2 and C2-3 joint), and (subtype: A-isolated bony injury; B-bony/ligamentous injury; C-displaced/translational injury), neurological status [N0-intact; N1-transient deficit; N2-radiculopathy; N3-incomplete spinal cord injury (SCI); N4-complete SCI, and NX-unable to examine], and case-specific modifiers (M1-injuries at risk of nonunion; M2-injuries at risk of instability; M3-patient specific factors; M4-vascular injury). MATERIALS AND METHODS: Totally, 151 AO Spine members (orthopaedic and neurosurgery) were surveyed globally regarding the severity (zero-low severity to 100-high severity) of each UCIC variable. Primary outcomes were differences in perceived injury severity score (ISS) over various geographic/practice settings, level of experience, and subspecialty. RESULTS: One hundred forty-eight responses were received. There was an increase in median perceived severity as each anatomic region (I-III) progressed from types A to B to C. Neurological status progressed similarly, except N1 and N2 were perceived similarly. Modifier M2 was perceived more severely than M3. There were no differences in ISS among levels of surgeon experience. There were small geographic differences with respondents from North and Central and South America perceiving types IC ( P =0.003), IIB ( P =0.003), and IIIB ( P =0.003) somewhat more severely than other regions. Neurosurgeons perceived types IB ( P =0.002) and IIIB ( P =0.026) as more severe than orthopaedic spine surgeons. CONCLUSIONS: The AO Spine UCIC has overall excellent hierarchical progression in subtype ISS. These findings are consistent across geographic regions, spine subspecialty training and experience levels.

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)0.021
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
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 head0.011
GPT teacher head0.302
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 source (direct Gemma or distilled Codex), 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

Citations4
Published2025
Admission routes1
Has abstractyes

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