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Record W1973327422 · doi:10.1097/brs.0000000000000666

Subaxial Injury Classification Scoring System Treatment Recommendations

2014· article· en· W1973327422 on OpenAlexaff
Sumant Samuel, Jiun-Lih Lin, Margaret M. Smith, Nathan Hartin, Con Vasili, Stephen J. Ruff, Andrew K. Cree, Jonathon Ball, Ioannis G. Sergides, Randolph Gray

Bibliographic record

VenueSpine · 2014
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicineCervical spineRetrospective cohort studyInjury Severity ScoreCervical spine injuryReferralPhysical therapySurgeryEmergency medicinePoison controlInjury prevention

Abstract

fetched live from OpenAlex

STUDY DESIGN: Retrospective case series. OBJECTIVE: To test validity of subaxial injury classification (SLIC) treatment recommendations. SUMMARY OF BACKGROUND DATA: Although SLIC has been tested for reliability, external studies that test the validity of its treatment recommendations are lacking. METHODS: The SLIC score was determined by reviewing imaging studies and clinical records in a consecutive series of 185 patients with subaxial cervical spine trauma presenting to a level 1 spinal injury referral center. Details including attending surgeon responsible for treatment decision, treatment received, and surgical approach were collected. RESULTS: Treatment received matched SLIC guidelines in 93.6% nonsurgically managed patients and 96.3% surgically managed patients. The mean SLIC score of the surgically treated group of patients was significantly higher than that of the nonsurgical group (7.14 vs. 2.22; P<0.001). Sixty-six patients had a SLIC score of 3 or less, and 94% of them were nonsurgically managed (P<0.001). One hundred two patients had a SLIC score of 5 or more, and 95% of them were surgically managed (P<0.001). Seventeen patients had a SLIC score of 4, and 65% were nonsurgically managed (P=0.032). Injury morphology scores were not predictive of surgical approach. Increasing SLIC scores correlated with increasing complexity of treatment (r=0.77; P<0.001). The distribution of patients with regard to severity of injuries and treatment delivered by the 7 spinal surgeons was comparable. The past practice of these 7 fellowship-trained spine surgeons was individually in agreement with SLIC treatment recommendations. CONCLUSION: Our past practice reflects SLIC treatment recommendations for nonsurgical treatment of patients with SLIC scores of 3 or less and surgical treatment of patients with SLIC scores of 5 or more. The use of SLIC as an ordinal severity scale is validated as increasing SLIC scores correlated with increasing complexity of treatment. The injury morphology score did not predict a surgical approach. Significantly higher numbers of patients with a SLIC score of 4 were treated nonsurgically. LEVEL OF EVIDENCE: 3.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.257

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 head0.034
GPT teacher head0.337
Teacher spread0.304 · 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 designOther design
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

Citations22
Published2014
Admission routes1
Has abstractyes

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