Differential Item Functioning on the Cochin Hand Function Scale Among People With Systemic Sclerosis by Language, Sex, and Disease Subtype: A Scleroderma <scp>Patient‐Centered</scp> Intervention Network (<scp>SPIN</scp>) Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the degree that the Cochin Hand Function Scale (CHFS) generates scores that are comparable across language, sex, and disease subtype. METHODS: We included participants enrolled in the Scleroderma Patient-centered Intervention Network (SPIN) Cohort who completed the CHFS at their baseline assessment between April 2014 and September 2020. Confirmatory factor analysis (CFA) was used to test unidimensionality, and multiple indicator multiple cause (MIMIC) models were used for differential item functioning (DIF) analysis based on language, sex, and disease subtype. Both intraclass correlation coefficient (ICC) and Pearson's correlation were calculated using factor scores obtained from unadjusted and DIF-adjusted MIMIC models to evaluate agreement and correlation between scores. RESULTS: [127] = 1,754.671; P < 0.001; Tucker-Lewis index = 0.985; comparative fit index = 0.987; root mean square error of approximation = 0.077). Nine items displayed statistically significant DIF for language of administration, 10 items for sex, and 10 items for disease subtype. However, the overall impact of DIF was negligible when comparing factor scores that did and did not account for DIF (ICC = 0.999; r = 0.999). CONCLUSION: The CHFS has score comparability in systemic sclerosis regardless of participants' language, sex, and disease subtype.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".