Functional Index-3: A Valid and Reliable Functional Outcome Assessment Measure in Patients With Dermatomyositis and Polymyositis
Bibliographic record
Abstract
OBJECTIVE: Patients with dermatomyositis (DM) and polymyositis (PM) have reduced muscle endurance.The aim of this study was to streamline the Functional Index-2 (FI-2) by developing the Functional Index-3 (FI-3) and to evaluate its measurement properties, content and construct validity, and intra- and interrater reliability. METHODS: A dataset of the previously performed and validated FI-2 (n = 63) was analyzed for internal redundancy, floor, and ceiling effects. The content of the FI-2 was revised into the FI-3. Construct validity and intrarater reliability of FI-3 were tested on 43 DM and PM patients at 2 rheumatology centers. Interrater reliability was tested in 25 patients. The construct validity was compared with the Myositis Activities Profile (MAP), Health Assessment Questionnaire (HAQ), and Borg CR-10 using Spearman correlation coefficient. RESULTS: Spearman correlation coefficients of 63 patients performing FI-3 revealed moderate to high correlations between shoulder flexion and hip flexion tasks and similar correlations with MAP and HAQ scores; there were lower correlations for neck flexion task. All FI-3 tasks had very low to moderate correlations with the Borg scale. Intraclass correlation coefficients (ICC) of FI-3 tasks for intrarater reliability (n = 25) were moderate to good (0.88-0.98). ICC of FI-3 tasks for interrater reliability (n = 17) were fair to good (range 0.83-0.96). CONCLUSION: The FI-3 is an efficient and valid method for clinically assessing muscle endurance in DM and PM patients. FI-3 construct validity is supported by the significant correlations between functional tasks and the MAP, HAQ, and Borg CR-10 scores.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".