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Record W2041474803 · doi:10.1097/brs.0b013e3182a8a381

AOSpine Thoracolumbar Spine Injury Classification System

2013· article· en· W2041474803 on OpenAlexaff
Alexander R. Vaccaro, F. Cumhur Öner, Christopher K. Kepler, Marcel F. Dvorak, Klaus John Schnake, Carlo Bellabarba, Maximilian Reinhold, Bizhan Aarabi, Frank Kandziora, Jens R. Chapman, Shanmuganathan Rajasekaran, Michael G. Fehlings, Luiz Roberto Vialle

Bibliographic record

VenueSpine · 2013
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsMedicineKappaCohen's kappaObservational studyReliability (semiconductor)Injury Severity ScoreGrading (engineering)Physical therapyPoison controlInjury preventionMachine learningPathologyMedical emergencyComputer science

Abstract

fetched live from OpenAlex

STUDY DESIGN: Reliability and agreement study, retrospective case series. OBJECTIVE: To develop a widely accepted, comprehensive yet simple classification system with clinically acceptable intra- and interobserver reliability for use in both clinical practice and research. SUMMARY OF BACKGROUND DATA: Although the Magerl classification and thoracolumbar injury classification system (TLICS) are both well-known schemes to describe thoracolumbar (TL) fractures, no TL injury classification system has achieved universal international adoption. This lack of consensus limits communication between clinicians and researchers complicating the study of these injuries and the development of treatment algorithms. METHODS: A simple and reproducible classification system of TL injuries was developed using a structured international consensus process. This classification system consists of a morphologic classification of the fracture, a grading system for the neurological status, and description of relevant patient-specific modifiers. Forty cases with a broad range of injuries were classified independently twice by group members 1 month apart and analyzed for classification reliability using the Kappa coefficient (κ). RESULTS: The morphologic classification is based on 3 main injury patterns: type A (compression), type B (tension band disruption), and type C (displacement/translation) injuries. Reliability in the identification of a morphologic injury type was substantial (κ= 0.72). CONCLUSION: The AOSpine TL injury classification system is clinically relevant according to the consensus agreement of our international team of spine trauma experts. Final evaluation data showed reasonable reliability and accuracy, but further clinical validation of the proposed system requires prospective observational data collection documenting use of the classification system, therapeutic decision making, and clinical follow-up evaluation by a large number of surgeons from different countries.

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)0.019
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.304
Teacher spread0.288 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations882
Published2013
Admission routes1
Has abstractyes

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