Evaluating High-Resolution Computed Tomography Derived 3-D Joint Space Metrics of the Metacarpophalangeal Joints Between Rheumatoid Arthritis and Age- and Sex-Matched Control Participants
Bibliographic record
Abstract
Abstract Rheumatoid arthritis associated joint space narrowing is commonly evaluated through 2D X-ray radiographs. Unfortunately, changes and overlapping anatomy in smaller joints, such as those found within the hands, hinder conventional radiography. High resolution peripheral quantitative computed tomography (HR-pQCT), an un-paralleled in vivo X-ray-based imaging technique, provides 3D quantitative joint space metrics that may overcome limitations of 2D imaging. However, whether these metrics are sufficient for the differentiation between RA-associated joint changes and those influenced by age, sex, and obesity remains unknown. Therefore, we recruited a cohort of RA patients as well as age- and sex-matched healthy control participants and scanned their 2nd and 3rd metacarpophalangeal joints using HR-pQCT. HR-pQCT-derived 3D joint space metrics (volume, width, standard deviation of width, maximum width, minimum width, and asymmetry) were not significantly different between RA and control groups (p > 0.05). This may be explained by the few RA participants with evidence of radiographic damage included in this study. Joint space volume, mean joint space width (JSW), maximum JSW, minimum JSW were larger in males than females (p < 0.05), while maximum JSW decreased with age. However, there were no significant association between joint space metrics and BMI. Thus, as individuals with RA are expected to have more joint space narrowing, further research is necessary to determine whether additional factors (e.g. co-morbidities) or novel 3D JSW metrics can aid in the detection of early signs of joint space.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".