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Record W3114170559 · doi:10.1097/brs.0000000000003873

Establishing the Injury Severity of Subaxial Cervical Spine Trauma

2020· article· en· W3114170559 on OpenAlexaff
Gregory D. Schroeder, José A. Canseco, Parthik D. Patel, Srikanth N. Divi, Brian A. Karamian, Frank Kandziora, Emiliano Vialle, F. Cumhur Öner, Klaus John Schnake, Marcel F. Dvorak, Jens R. Chapman, Lorin M. Benneker, Shanmuganathan Rajasekaran, Christopher K. Kepler, Alexander R. Vaccaro

Bibliographic record

VenueSpine · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsVancouver General Hospital
FundersAO Foundation
KeywordsMedicineCervical spine injuryInjury Severity ScoreSpinal cord injuryCervical spineCervical vertebraeAbbreviated Injury ScaleFacet (psychology)Injury preventionPoison controlPhysical therapySurgeryEmergency medicineSpinal cord

Abstract

fetched live from OpenAlex

STUDY DESIGN: Global cross-sectional survey. OBJECTIVE: The aim of this study was to validate the AO Spine Subaxial Cervical Spine Injury Classification by examining the perceived injury severity by surgeon across AO geographical regions and practice experience. SUMMARY OF BACKGROUND DATA: Previous subaxial cervical spine injury classifications have been limited by subpar interobserver reliability and clinical applicability. In an attempt to create a universally validated scheme with prognostic value, AO Spine established a subaxial cervical spine injury classification involving four elements: injury morphology, facet injury involvement, neurologic status, and case-specific modifiers. METHODS: A survey was sent to 272 AO Spine members across all geographic regions and with a variety of practice experience. Respondents graded the severity of each variable of the classification system on a scale from zero (low severity) to 100 (high severity). Primary outcome was to assess differences in perceived injury severity for each injury type over geographic regions and level of practice experience. RESULTS: A total of 189 responses were received. Overall, the classification system exhibited a hierarchical progression in subtype injury severity scores. Only three subtypes showed a significant difference in injury severity score among geographic regions: F3 (floating lateral mass fracture, P = 0.04), N3 (incomplete spinal cord injury, P = 0.03), and M2 (critical disk herniation, P = 0.04). When stratified by surgeon experience, pairwise comparison showed only two morphological subtypes, B1 (bony posterior tension band injury, P = 0.02) and F2 (unstable facet fracture, P = 0.03), and one neurologic subtype (N3, P = 0.02) exhibited a significant difference in injury severity score. CONCLUSION: The AO Spine Subaxial Cervical Spine Injury Classification System has shown to be reliable and suitable for proper patient management. The study shows this classification is substantially generalizable by geographic region and surgeon experience, and provides a consistent method of communication among physicians while covering the majority of subaxial cervical spine traumatic injuries.Level of Evidence: 4.

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)0.000
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 score0.652
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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

Citations34
Published2020
Admission routes1
Has abstractyes

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