The Minimal Erosive Volume Needed for Radiographic Identification of Erosions in the Metacarpophalangeal Joints in Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
Objective To compare in images, obtained by high-resolution peripheral quantitative computed tomography (HR-pQCT) and conventional radiography (CR) of the second and third metacarpophalangeal (MCP) joints, the minimal erosive cortical break needed to differentiate between pathological and physiological cortical breaks. Methods In this single-center cross-sectional study, patients with established rheumatoid arthritis (disease duration ≥ 5 yrs) had their second and third MCP joints of the dominant hand investigated by HR-pQCT and CR. Empirical estimation was used to find the optimal cut-off value for the number of erosions and total erosive volume, which were detectable between patients with and without erosions in the second and third MCP joints according to CR. Results The total erosive volume in the second and third MCP joints by HR-pQCT for CR-detected erosive disease was estimated to be 56.4 mm3(95% CI 3.5-109.3). The sensitivity and specificity at this cutpoint were 78% and 83%, respectively, with an area under the receiver-operating characteristic curve (AUC) of 0.81. The optimal cut-off value for the number of erosions by HR-pQCT was 8.5 (95% CI 5.9-11.1) for CR-detected erosive disease in the second and third MCP joints. The sensitivity and specificity at this cutpoint were 74% and 88%, respectively, with an AUC of 0.81. Conclusion Erosions by HR-pQCT were larger in patients with erosive damage in the second and third MCP joints by CR. We found that CR had poor sensitivity for detecting erosive disease when the erosive volume was < 56.4 mm3or the number of erosions was < 8.5.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".