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Record W4400690116 · doi:10.1123/jab.2024-0006

Measurement Position Influences Sex Comparisons of Distal Femoral Cartilage Thickness With Ultrasound Imaging

2024· article· en· W4400690116 on OpenAlexaff
Harry S. Battersby, Ryan J. Evans, Iwi J. Eghobamien, Derek N. Pamukoff

Bibliographic record

VenueJournal of Applied Biomechanics · 2024
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsWestern University
Fundersnot available
KeywordsUltrasoundPosition (finance)MedicineBiomedical engineeringUltrasound imagingCartilageArticular cartilageOrthodonticsAnatomyRadiologyOsteoarthritisPathology

Abstract

fetched live from OpenAlex

The purpose was to examine (1) the effect of measurement position and sex on femoral cartilage outcomes, and (2) the association between gait biomechanics and cartilage outcomes. Fifty individuals participated (25 males and 25 females; age = 20.62 [1.80] y). Ultrasound measured femoral cartilage thickness and echo-intensity at 90°, 115°, and 140° of knee flexion. Gait outcomes included the external knee adduction and knee flexion moments. Cartilage outcomes were compared using 2 (sex) × 3 (position) repeated-measures analysis of variance. Gait and cartilage associations were assessed using stepwise regression. Medial cartilage was thicker when measured at 90° compared with 115° (P = .02) and 140° (P < .01), and 115° compared with 140°, (P < .01) in males but not in females. Cartilage was thicker at 90° compared with 140° across both sexes within all regions (P < .01). Males had thicker cartilage than females in all positions (P < .01). Echo-intensity was lower at 90° than 115° (P < .01) and 140° (P = .01) in the central and lower at 90° than at 115° (P < .01) and 140° (P = .03) in lateral regions. No association was found between gait and cartilage outcomes. Ultrasound imaging position effects cartilage features more in males compared with females. Imaging position and sex influence cartilage outcomes and should be considered in study designs and clinical evaluation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.495

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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 designBench or experimental
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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