An automated algorithm for the detection of cortical interruptions and its underlying loss of trabecular bone; a reproducibility study
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Bibliographic record
Abstract
We developed a semi-automated algorithm that detects cortical interruptions in finger joints using high-resolution peripheral quantitative computed tomography (HR-pQCT), and extended it with trabecular void volume measurement. In this study we tested the reproducibility of the algorithm using scan/re-scan data. Second and third metacarpophalangeal joints of 21 subjects (mean age 49 (SD 11) years, 17 early rheumatoid arthritis and 4 undifferentiated arthritis, all diagnosed < 1 year ago) were imaged twice by HR-pQCT on the same day with repositioning between scans. The images were analyzed twice by one operator (OP1) and once by an additional operator (OP2), who independently corrected the bone contours when necessary. The number, surface and volume of interruptions per joint were obtained. Intra- and inter-operator reliability and intra-operator reproducibility were determined by intra-class correlation coefficients (ICC). Intra-operator reproducibility errors were determined as the least significant change (LSCSD). Per joint, the mean number of interruptions was 3.1 (SD 3.6), mean interruption surface 4.2 (SD 7.2) mm2, and mean interruption volume 3.5 (SD 10.6) mm3 for OP1. Intra- and inter-operator reliability was excellent for the cortical interruption parameters (ICC ≥0.91), except good for the inter-operator reliability of the interruption surface (ICC = 0.70). The LSCSD per joint was 4.2 for the number of interruptions, 5.8 mm2 for interruption surface, and 3.2 mm3 for interruption volume. The algorithm was highly reproducible in the detection of cortical interruptions and their volume. Based on the LSC findings, the potential value of this algorithm for monitoring structural damage in the joints in early arthritis patients needs to be tested in clinical studies.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it