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Record W2158691287 · doi:10.1080/17453670710015436

A review of the TLICS system: a novel, user-friendly thoracolumbar trauma classification system

2008· review· en· W2158691287 on OpenAlexaff
Jeffrey A. Rihn, D. T. Anderson, Eric Harris, James P. Lawrence, Håkan Jönsson, Jared Wilsey, R. John Hurlbert, Alexander R. Vaccaro

Bibliographic record

VenueActa Orthopaedica · 2008
Typereview
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsUniversity of CalgaryMedtronic (Canada)
Fundersnot available
KeywordsMedicineInjury Severity ScoreReliability (semiconductor)Physical medicine and rehabilitationPhysical therapyInjury preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

The classification and treatment of thoracolumbar injuries remain controversial. The Spine Trauma Study Group (STSG) has developed a classification system that has prognostic significance and helps guide treatment decisions. It is based on three aspects: morphology of the injury, integrity of the posterior ligamentous complex, and neurological status of the patient. A severity score is used in conjunction with the classification system to help guide treatment decisions. This classification system has been shown to have good inter- and intra-observer reliability.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0090.010
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.047
GPT teacher head0.342
Teacher spread0.295 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations58
Published2008
Admission routes1
Has abstractyes

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