Perceptions of Health Status in Multiple Sclerosis Patients and Their Doctors
Bibliographic record
Abstract
OBJECTIVE: To compare neurologist and patient perceptions of multiple sclerosis (MS)-related health status. METHODS: MS patients (n=99) were recruited from six sites in Canada. Following a consultation with their neurologist, patients estimated their relapse frequency, rated their general health and quality of life (QoL), reviewed descriptions of eight health domains and selected the three most important, and completed a utility assessment using the standard gamble (SG). Concurrently, neurologists independently used the same instruments to rate their patients' health status. Assessments were compared on the basis of paired mean values of both groups and the degree of exact agreement quantified by intraclass coefficient (ICC) and kappa analyses, which yield values of 1.0 with 100% agreement. RESULTS: There were significant differences (p<0.001) between patient and neurologist ratings for relapses in the last year (0.86 vs. 0.4, respectively), QoL (61.2 vs. 69.7 (maximum score = 100) and utility (0.864 vs. 0.971); ICC analysis revealed moderate to poor levels of agreement (0.56 for QoL to 0.03 for SG). There was little concordance in identification of important health domain and the only significant associations were in bodily pain and social functioning (kappa statistic = 0.24, p = 0.026 for both). Neurologists identified physical functioning domains as important, while patients placed more emphasis on mental health domains. CONCLUSIONS: Discrepancies between neurologist and patient perceptions of MS were observed. The study identifies a need to educate neurologists on the recognition of MS health domains that are important in the definition of patient QoL.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".