Quality of life in multiple sclerosis: translation in French Canadian of the MSQoL-54.
Bibliographic record
Abstract
BACKGROUND: Multiple Sclerosis (MS) is a neurodegenerative disease which runs its course for the remainder of the patient's life frequently causing disability of varying degrees. Negative effects on Health-related quality of life (HRQOL) are well documented and a subject of clinical study. The Multiple Sclerosis QOL 54 (MSQOL-54) questionnaire was developed to measure HRQOL in patients with MS. It is composed of 54 items, and is a combination of the SF-36 and 18 disease-specific items. OBJECTIVE: The objective of this project was to translate the MSQOL-54 into French Canadian, and to make it available to the Canadian scientific community for clinical research and clinical practice. METHODS: Across all French speaking regions, there are occurrences of variation. They include the pronunciation, sentence structure, and the lexicon, where the differences are most marked. For this reason, it was decided to translate the US original MSQOL-54 into French Canadian instead of adapting the existing French version. The SF-36 has been previously validated and published in French Canadian, therefore the translation work was performed solely on the 18 MS specific items. The translation followed an internationally accepted methodology into 3 steps: forward translation, backward translation, and patients' cognitive debriefing. RESULTS: Instructions and Items 38, 43, 45 and 49 were the most debated. Problematic issues mainly resided in the field of semantics. Patients' testing (n = 5) did not reveal conceptual problems. The questionnaire was well accepted, with an average time for completion of 19 minutes. CONCLUSION: The French Canadian MSQOL-54 is now available to the Canadian scientific community and will be a useful tool for health-care providers to assess HRQOL of patients with MS as a routine part of clinical practice. The next step in the cultural adaptation of the MSQOL-54 in French Canadian will be the evaluation of its psychometric properties.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".