Development of a Bilingual MS-Specific Health Classification System
Bibliographic record
Abstract
OBJECTIVE: The global aim of this study was to contribute to the development of the Preference-Based Multiple Sclerosis Index (PBMSI). The specific objective of this foundational work was to qualitatively review the items selected for inclusion in the PBMSI using expert and patient feedback. METHODS: Cognitive interviews were conducted with patients with multiple sclerosis (MS) in English and French. The verbal probing method was used to conduct the interviews. For each PBMSI item, the interviewer probed for specific information on what types of difficulty participants had with the item and the basis for their response for each item. Furthermore, respondents were asked to provide information on the clarity of the item, the meaning of the item, the appropriateness of the response options, and the recall period. All interviews were recorded using a digital voice recorder and were transcribed onto a computer. RESULTS: The mean age of the 22 respondents was 52 years, and 82% were women. Mean time since diagnosis was 12 years, and the highest level of education completed was university or college for 86% of the sample. Modifications were made to each item in terms of recall period, instructions, and phrasing. CONCLUSIONS: Patient and expert feedback allowed us to clarify items, simplify language, and make items more uniform in terms of their instructions and response options. This qualitative review process will increase accuracy of reporting and reduce measurement error for the PBMSI.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".