Reliability and Longitudinal Validity of Computer-assisted Methods for Measuring Joint Damage Progression in Subjects with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To compare the metric properties of a computer-assisted erosion segmentation volume measurement with scoring using the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) in a longitudinal cohort of patients with rheumatoid arthritis (RA). METHODS: Thirty-two sets of baseline and 2-year followup magnetic resonance imaging (MRI) of metacarpal phalangeal 2-5 joints of patients with RA were scored using RAMRIS and segmented using OSIRIS software. The smallest detectable difference (SDD), standardized response mean (SRM), and paired t-test were used to evaluate the sensitivity to change. Eleven of the 32 patients' MRI were segmented by both readers to evaluate interreader agreement. The 28-joint Disease Activity Score (DAS28) and Sharp erosion scores further evaluated construct and longitudinal validity. RESULTS: Reliability of erosion progression by computer-assisted volume measurement was superior to RAMRIS [intrareader interclass correlation coefficient (ICC) 0.97 (0.94-0.99) vs 0.52 (0.22-0.73)] and interreader ICC of volume measurement was 0.85 (0.53-0.96). Computer-assisted volume measurements identified 10 of 32 patients who progressed more than the SDD progression, whereas RAMRIS identified only 4 of 32 patients (p = 0.0013). By a paired t-test, however, all MRI measures progressed significantly over 2 years (irrespective of treatment arm) and there was little difference by SRM. Construct correlational validity of the MRI methods was 0.47-0.90 for status scores and 0.33-0.81 for progression. There was no relationship between the average DAS28 and erosion progression by any imaging method. CONCLUSION: Computer-assisted measurement of erosion volume has good performance metrics. It had excellent intrareader and interreader reliability and was more sensitive to change than RAMRIS in this group of patients. www.ClinicalTrials.gov, NCT00451971.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".