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Record W7117416221 · doi:10.1093/jbmrpl/ziaf196

Measurement of knee joint space width with bi-planar radiography

2025· article· en· W7117416221 on OpenAlexafffund
Isabella Derij Vandergaag, R. L. Walker, Steven K Boyd

Bibliographic record

VenueJBMR Plus · 2025
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of CalgaryAlberta Bone and Joint Health Institute
FundersCanadian Institutes of Health Research
KeywordsRadiographyConventional radiographyKnee JointPosition (finance)Joint (building)

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to identify whether the joint space width (JSW) of the knee, measured by bi-planar imaging, is reproducible compared to the clinical reference radiography. Our cross-sectional study design included a cohort of uninjured individuals (N = 30, 26.7 ± 5.1 yr) who underwent scanning to determine the short-term precision of the technique, involving repeat scans by bi-planar radiograph. Additionally, repeat conventional tunnel view knee radiographs were used as a comparator. The minimum apparent tibiofemoral JSW was collected for each leg side and compartment for both modalities. The root-mean-square coefficient of variation (RMSCV) and least significant change (LSC) for bi-planar scans (RMSCV = 6.46%, LSC = 1.07 mm) were comparable to conventional radiography (CR) (RMSCV = 7.66%, LSC = 1.15 mm). There was a bias for greater JSW by bi-planar radiography than CR (9.0%, p < .01), particularly for the forward unloaded left leg lateral (15.5%) and medial (17.6%) compartments. In conclusion, we found that JSW measurements from bi-planar scanners are reproducible and comparable to CR. While radiography remains accessible clinically, accurate and precise JSW by bi-planar scanners is feasible provided knee position and alignment are controlled.

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 score0.120
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.238
Teacher spread0.223 · 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
Published2025
Admission routes2
Has abstractyes

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