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Record W4391321407 · doi:10.1097/brs.0000000000004936

Detection and Characterization of Endplate Structural Defects on CT

2024· article· en· W4391321407 on OpenAlexaff
Aliyu Lawan, Andrew Leung, Stephanie Leung, James Faul, Joseph Umoh, David W. Holdsworth, Dianne Bryant, Michele C. Battié

Bibliographic record

VenueSpine · 2024
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsVictoria HospitalLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsKappaMedicineSagittal planeNuclear medicineComputed tomographyCadaverRadiologyAnatomyMathematicsGeometry

Abstract

fetched live from OpenAlex

STUDY DESIGN: Diagnostic test study. OBJECTIVE: To determine the reliability and validity or diagnostic accuracy of two previously described endplate structural defect (EPSD) assessment methods. SUMMARY OF BACKGROUND DATA: Studies of EPSD may further the understanding of pathoanatomic mechanisms underlying back pain. However, clinical imaging methods used to document EPSD have not been validated, leaving uncertainty about what the observations represent. MATERIALS AND METHODS: Using an evaluation manual, 418 endplates on CT sagittal slices obtained from 19 embalmed cadavers (9 men and 10 women, aged 62-91 yr) were independently assessed by two experienced radiologists and a novice for EPSD using the two methods. The corresponding micro-CT (µCT) from the harvested T7-S1 spines were assessed by another independent rater with excellent intra-rater reliability (k=0.96). RESULTS: Inter-rater reliability was good for the presence (k=0.60-0.69) and fair for specific phenotypes (k=0.43-0.58) of EPSD. Erosion, for which the Brayda-Bruno classification lacked a category, was mainly (82.8%) classified as wavy/irregular, while many notched defects (n=15, 46.9%) and Schmorl's nodes (n=45, 79%) were recorded as focal defects using Feng's classification. When compared to µCT, endplate fractures (n=53) and corner defects (n=28) were routinely missed on CT. Endplates classified as wavy/irregular on CT corresponded to erosion (n=29, 21.2%), jagged defects (n=21, 15.3%), calcification (n=19, 13.9%), and other phenotypes on µCT. Some focal defects on CT represented endplate fractures (n=21, 27.6%) on µCT. Overall, with respect to the presence of an EPSD, there was a sensitivity of 70.9% and a specificity of 79.1% using Feng's method, and 79.5% and 57.5% using Brayda-Bruno's method. Poor to fair inter-rater reliability (k=0.26-0.47) was observed for defect dimensions. CONCLUSION: There was good inter-rater reliability and evidence of criterion validity supporting assessments of EPSD presence using both methods. However, neither method contained all needed EPSD phenotypes for optimal sensitivity, and specific phenotypes were often misclassified.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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