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A New Classification of Thoracolumbar Injuries

2005· article· en· W2049957494 on OpenAlexaff
Alexander R. Vaccaro, Ronald A. Lehman, R. John Hurlbert, Paul A. Anderson, Mitchel B. Harris, Rune Hedlund, James S. Harrop, Marcel F. Dvorak, Kirkham B. Wood, Michael G. Fehlings, Charles G. Fisher, Steven C. Zeiller, D. Greg Anderson, Christopher M. Bono, Gordon H. Stock, Andrew K. Brown, Timothy R. Kuklo, F C. ner

Bibliographic record

VenueSpine · 2005
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsFoothills Medical CentreUniversity of CalgaryUniversity of TorontoUniversity Health NetworkUniversity of British Columbia
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

In Brief Study Design. A new proposed classification system for thoracolumbar (TL) spine injuries, including injury severity assessment, designed to assist in clinical management. Objective. To devise a practical, yet comprehensive, classification system for TL injuries that assists in clinical decision-making in terms of the need for operative versus nonoperative care and surgical treatment approach in unstable injury patterns. Summary of Background Data. The most appropriate classification of traumatic TL spine injuries remains controversial. Systems currently in use can be cumbersome and difficult to apply. None of the published classification schemata is constructed to aid with decisions in clinical management. Methods. Clinical spine trauma specialists from a variety of institutions around the world were canvassed with respect to information they deemed pivotal in the communication of TL spine trauma and the clinical decision-making process. Traditional injury patterns were reviewed and reconsidered in light of these essential characteristics. An initial validation process to determine the reliability and validity of an earlier version of this system was also undertaken. Results. A new classification system called the Thoracolumbar Injury Classification and Severity Score (TLICS) was devised based on three injury characteristics: 1) morphology of injury determined by radiographic appearance, 2) integrity of the posterior ligamentous complex, and 3) neurologic status of the patient. A composite injury severity score was calculated from these characteristics stratifying patients into surgical and nonsurgical treatment groups. Finally, a methodology was developed to determine the optimum operative approach for surgical injury patterns. Conclusions. Although there will always be limitations to any cataloging system, the TLICS reflects accepted features cited in the literature important in predicting spinal stability, future deformity, and progressive neurologic compromise. This classification system is intended to be easy to apply and to facilitate clinical decision-making as a practical alternative to cumbersome classification systems already in use. The TLICS may improve communication between spine trauma physicians and the education of residents and fellows. Further studies are underway to determine the reliability and validity of this tool. A new thoracolumbar trauma classification system is described called the Thoracolumbar Injury Classification and Severity Score (TLICS). It is composed of three main components: 1) morphology of injury according to radiographic features, 2) integrity of the posterior ligamentous complex, and 3) neurologic status of the patient. This new classification scheme accounts for predictors of spinal stability, future deformity, and progressive neurologic compromise, thereby facilitating clinical decision making.

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)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.338
Teacher spread0.318 · 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 designNot applicable
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

Citations867
Published2005
Admission routes1
Has abstractyes

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