Validation of the preference-based multiple sclerosis index
Bibliographic record
Abstract
BACKGROUND: Preference-based measures of health-related quality of life (HRQL) are used as primary or secondary endpoints in multiple sclerosis (MS) research. OBJECTIVE: The purpose of this paper was to evaluate the structural, convergent, and known-groups validity of the preference-based multiple sclerosis index (PBMSI) of HRQL in people with MS. METHODS: Participants were recruited from three MS clinics in Montreal. Structural validity was assessed using polychoric correlation coefficients and factor analysis. To assess convergent validity, hypotheses were formulated about the strength of correlations between the PBMSI and other HRQL measures. Known-groups validity was assessed against different measures of disability. RESULTS: The average age of the sample was 46 and 77% were women. Factor analysis supported the structural validity of the PBMSI; the items collectively were measuring one underlying construct. The PBMSI showed convergent validity against generic measures of HRQL, and known-groups validity between persons with different levels of disability. CONCLUSION: The results of this study support the construct validity of the PBMSI as an outcome measure of HRQL in MS. The PBMSI overcomes limitations observed with currently used HRQL measures in MS and may be used to contrast different interventions for people with MS.
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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 | 0.012 | 0.029 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".