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Record W4399154697 · doi:10.1093/mr/roae054

Diagnostic values and relevant factors of lumbar posterior lesions in axial spondyloarthritis

2024· article· en· W4399154697 on OpenAlexaboutno aff
Zhixiang Huang, Jiali Ding, Xuechan Huang, Yukai Huang, Xin Guo, Shuyang Chen, Weiming Deng, Li T

Bibliographic record

VenueModern Rheumatology · 2024
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAxial spondyloarthritisSpondylarthritisLumbarRheumatologyInternal medicinePhysical therapyAnkylosing spondylitisRadiologySacroiliitis

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare the lumbar posterior lesions between axial spondyloarthritis (axSpA) and lumbar disc herniation (LDH) patients, then their diagnostic value and related factors were evaluated. METHODS: This cross-sectional study included axSpA patients from January 2020 to September 2023. They were classified as ankylosing spondylitis (AS) and non-radiographic axSpA (nr-axSpA) individuals. Canada-Denmark magnetic resonance imaging (MRI) scoring system was used to assess the defects of the lumbar spine. Receiver operating characteristic curve analysis was utilized to determine the value of distinguishing nr-axSpA. Linear regression analyses were adopted to find the relevant factors for lumbar posterior lesions. RESULTS: Ninety-six AS, 98 nr-axSpA, and 108 LDH patients were included. The Canada-Denmark scores were greater in axSpA patients, AS in particular. Furthermore, lumbar posterior lesions can distinguish AS, nr-axSpA, and LDH. Besides, lumbar posterior lesions were positively related to the similar MRI changes in their adjacent structures, but were inversely associated with the other abnormalities. CONCLUSIONS: Lumbar posterior lesions were more serious in axSpA patients. These alterations had value in distinguishing axSpA. Lumbar posterior defects were related to their adjacent components, and they may not fully follow the MRI changing pattern of vertebral bodies and sacroiliac joints.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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