The SPECTRA Collaboration OMERACT Special Interest Group: Current Research and Future Directions
Bibliographic record
Abstract
OBJECTIVE: High-resolution peripheral quantitative computed tomography (HR-pQCT) has the potential to improve radiographic progression determination in clinical trials and longitudinal observational studies. The goal of this work was to describe the current state of research presented at Outcome Measures in Rheumatology (OMERACT) 2016 and ensuing future directions outlined during discussion among attendees. METHODS: At OMERACT 2016, SPECTRA (Study grouP for xtrEme-Computed Tomography in Rheumatoid Arthritis) introduced efforts to (1) validate the HR-pQCT according to OMERACT guidelines, focusing on rheumatoid arthritis (RA), and (2) find alternatives for automated joint space width (JSW) analysis. The Special Interest Group (SIG) was presented to patient research partners, physicians/researchers, and SIG leaders followed by a 40-min discussion on future directions. RESULTS: A consensus definition for RA erosion using HR-pQCT was demonstrated through a systematic literature review and a Delphi exercise. Histopathology and perfusion studies were presented that analyzed the true characteristics of cortical breaks in HR-pQCT images, and to provide criterion validity. Results indicate that readers were able to discriminate between erosion and small vascular channels. Moderate reliability (ICC 0.206-0.871) of direct erosion size measures was shown, which improved (> 0.9) only when experienced readers were considered. Quantification of erosion size was presented for scoring, direct measurement, and volumetric approaches, as well as a reliability exercise for direct measurement. Three methods for JSW measurement were compared, all indicating excellent reproducibility with differences at the extremes (i.e., near-zero and joint edge thickness). CONCLUSION: Initial reports on HR-pQCT are promising; however, to consider its use in clinical trials and longitudinal observational studies, it is imperative to assess the responsiveness of erosion measurement quantification.
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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 | 0.057 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.019 |
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".