The Dynamics of Quality of Life in ALS Patients and Caregivers
Bibliographic record
Abstract
BACKGROUND: Quality of life (QOL) in people with amyotrophic lateral sclerosis (ALS) and their caregivers may depend on disease progression, premorbid characteristics (e.g., personality or demographics), or idiosyncratic effects (e.g., life events unrelated to the disease). Furthermore, effects may differ for patients and caregivers; physical decline may impact the caregiver more than the patient. PURPOSE: The present study examined QOL in ALS patients and their caregivers over the course of the illness. METHODS: Longitudinal data from ALS patients (N = 55) and caregivers (N = 53) yielded estimates of the sources of and changes over time in total QOL as well as individual domains (psychological existential, physical, and social) as measured by the McGill Quality of Life Questionnaire. RESULTS: For both patients and caregivers, about half of QOL variance emerged from stable individual differences. Passage of time did not affect QOL in patients, but total QOL and particularly QOL related to physical symptoms declined over time in caregivers. Gender was mostly unrelated to QOL in patients and caregivers, but younger caregivers had lower QOL across a number of domains. CONCLUSIONS: Low QOL among ALS patients is likely due to pre-existing individual differences, whereas both individual differences such as demographics (e.g., age) and disease progression are likely to affect QOL among caregivers.
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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 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".