Reliability and Change in Erosion Measurements by High-resolution Peripheral Quantitative Computed Tomography in a Longitudinal Dataset of Rheumatoid Arthritis Patients
Bibliographic record
Abstract
Objective. The aim of this multireader exercise was to assess the reliability and change over time of erosion measurements in patients with rheumatoid arthritis (RA) using high-resolution peripheral quantitative computed tomography (HR-pQCT). Methods. HR-pQCT scans of 23 patients with RA were assessed at baseline and 12 months. Four experienced readers examined the dorsal, palmar, radial, and ulnar surfaces of the metacarpal head (MH) and phalangeal base (PB) of the second and third digits, blinded to time order. In total, 368 surfaces (23 patients´ 16 surfaces) were evaluated per timepoint to characterize cortical breaks as pathological (erosion) or physiological, and to quantify erosion width and depth. Reliability was evaluated by intraclass correlation coefficients (ICC), percentage agreement, and Light k; change over time was defined by means ± SD of erosion numbers and dimensions. Results. ICC for the mean measurements of width and depth of the pathological breaks ranged between 0.819–0.883, and 0.771–0.907, respectively. Most physiological cortical breaks were found at the palmar PB, whereas most pathological cortical breaks were located at the radial MH. There was a significant increase in both the numbers and the dimensions of erosions between baseline and follow-up (P= 0.0001 for erosion numbers, width, and depth in axial plane;P= 0.001 for depth in perpendicular plane). Conclusion. This exercise confirmed good reliability of HR-pQCT erosion measurements and their ability to detect change over time.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".