Cost and health related quality of life consequences of multiple sclerosis
Bibliographic record
Abstract
OBJECTIVES: To (i) quantify the cost of multiple sclerosis (MS) to the Canadian health care system and society; (ii) measure health utility in MS patients, and (iii) examine the influence of disability on patient utility and health care costs. MATERIALS AND METHODS: A comprehensive patient survey and chart review of relapsing MS patients in remission, relapse and recalling a relapse. RESULTS: Annual remission costs increased with EDSS level ($7596 at EDSS 1, $33 206 at EDSS 6). At all EDSS levels the largest costs were due to inability to work, which increased with EDSS. The average relapse cost for all EDSS levels was $1367. An inverse correlation was found between EDSS level and patient utility for patients in remission and relapse. The decrease in remission health utility from EDSS 1 to 6 was 0.24, which is 25% greater than the difference in health status between an average 25 and 85 year-old. CONCLUSIONS: This study demonstrates that MS produces substantial health care costs and reductions in patient quality of life and ability to work, losses that can be avoided or delayed if disease progression is slowed. These data provide health-care decision-makers with the opportunity to consider the full impact of MS when faced with budget allocation decisions.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".