The Development of EERA: Software for Assessing Rheumatic Joint Erosions
Bibliographic record
Abstract
OBJECTIVE: The principal aim of this study was to create a segmentation program, to be used by nonmusculoskeletal or junior fellows, that defines the bones in the metacarpophalangeal joint in a dynamic 3-dimensional image that will lead to higher inter-reader agreement of bone erosion scores. METHODS: The second to fifth metacarpal head and phalangeal bases of 15 participants were rated according to the Rheumatoid Arthritis Magnetic Resonance Imaging Scoring system by one trained and one untrained reader. Two comparisons were made. The first comparison was between the 2 readers using only the traditional 2-dimensional magnetic resonance image set. The second comparison was between the 2 readers, with the untrained reader using a custom segmentation program with traditional 2-dimensional magnetic resonance image set. RESULTS: The software marginally increased inter-reader reliability with the exception of the second metacarpal head, for which reliability was increased substantially. Future work will concentrate on improving image acquisition, better delineate erosions from surrounding bone oedema, and address methods to directly determine erosion volumes. CONCLUSIONS: Software designed to display dynamic 3-dimensional images enables a relatively untrained user to score the metacarpophalangeal joints in the hand for erosions equivalent to that produced by an expert using the manual methods.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".