Comparison of radiographic scoring methods in a cohort of RA patients treated with anti-TNF therapy
Bibliographic record
Abstract
OBJECTIVE: To compare the ability of the simple erosion narrowing score (SENS) to classify radiographic progression relative to the Sharp/van der Heijde score (SHS) in a prospective cohort of anti-TNF-treated RA patients. METHODS: Radiographs of the hands, wrists and feet of patients enrolled in a pharmacovigilance programme are performed every 2 years. These radiographs were read in chronological order by three rheumatologists and scored using the SHS. SENS scores were derived from the SHS. Additionally, one rheumatologist scored the radiographs using the SENS method only. Patients with radiographic progression in excess of the smallest detectable change were classified as progressors. The probability of agreement and κ-value between the SHS and SENS methods for determining progression was calculated. RESULTS: A sample of 25 patients was selected from the database. The annualized mean (s.d.) change in SHS score was 6.61 U (7.48 U) and in SENS score was 2.27 U (2.17 U). Five patients were classified as progressors using SHS and seven using SENS, with a probability of agreement of 84% (κ = 0.565). CONCLUSION: The SENS method captures radiographic progression reliably compared with the more detailed SHS method. SENS is suitable for application in clinical practice or in observational cohorts.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".