Validating the 28-Tender Joint Count Using Item Response Theory
Bibliographic record
Abstract
OBJECTIVE: To examine the construct validity of the 28-tender joint count (TJC-28) using item response theory (IRT)-based methods. METHODS: A total of 457 patients with early stage rheumatoid arthritis (RA) were included. Internal construct validity of the TJC-28 was evaluated by determining whether the TJC-28 fit a 2-measure logistic IRT model. As well, we tested whether the discrimination and difficulty parameters of the joints properly reflected the known left-right symmetry of joint involvement. External validity was evaluated by correlations with other established measures of disease activity, including pain, disability, general health, erythrocyte sedimentation rate (ESR), and the 28-swollen joint count. RESULTS: The TJC-28 showed a good fit with the 2-parameter logistic model, with no relevant differential item functioning across sex, age, and time and with excellent reliability. The 28 joints covered a reasonable range of disease activity, even though they were mainly targeted at patients with moderate or high disease activity levels. The joint parameters reflected the left-right symmetry of joint involvement for all pairs of joints except one. All disease activity measures, except ESR, were significantly correlated with the TJC-28. Most correlations were of the expected magnitude. CONCLUSION: The TJC-28 showed good internal and acceptable external construct validity for patients with early-stage RA. The IRT analyses did point to some potential limitations of the instrument, a major problem being its limited measurement range. Future research should examine whether instrument modifications might lead to a more robust assessment of disease activity in patients with RA.
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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 | 0.042 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".