Longitudinal Assessment of Health-Related Quality of Life (HRQL) of Patients with Multiple Sclerosis
Bibliographic record
Abstract
Abstract Over the past decade, health-related quality of life (HRQL) has become an important tool for assessing health status. To evaluate factors that could influence changes in HRQL over time, we surveyed multiple sclerosis (MS) patients who had completed the Medical Outcomes Trust 36-item Health Survey (SF-36) as part of a cross-sectional analysis of HRQL in 1994. Of the 100 subjects in the original study, 45 provided follow-up surveys in 1998. The 1998 Expanded Disability Status Scale (EDSS) score was obtained from patient records. Disease severity (mild, moderate, or severe) was derived from the EDSS score. Differences from baseline scores for the eight domains and the two summary scales of the SF-36 were calculated. Patients were stratified by sex, baseline disease severity, and change in disease severity, to determine if any of these factors were associated with changes in HRQL. A significant decline in health status was observed in the physical functioning, role physical, general health, and social functioning domains and on the physical component summary of the SF-36. In contrast, slightly over half the group showed an improvement in the mental health domain. Baseline disease severity was not significantly associated with changes in HRQL. However, change in disease severity was associated with change in the role emotional domain. Also, men showed significantly more deterioration than women in the role emotional domain. There were several meaningful changes in HRQL over time in this small sample. Additional studies are warranted to investigate associated factors. This may lead to a better understanding of HRQL for MS patients, and ultimately to improved patient care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".