The SEIQoL-DW for assessing quality of life in ALS: Strengths and limitations
Bibliographic record
Abstract
The Schedule for the Evaluation of the Individual Quality of Life-Direct Weighting (SEIQoL-DW) has been used to measure quality of life (QoL) in small cohorts of individuals with ALS, but its suitability for assessing aggregate QoL for between-group comparisons is uncertain. We undertook a prospective study in which 120 patients with ALS completed two measures of QoL, the SEIQoL-DW and the McGill Quality of Life Single-Item Scale (MQoL-SIS). There was a weak correlation between the SEIQoL-DW index score and the MQoL-SIS. Only three of five cues accounted for a significant amount of variance in the MQoL-SIS, and even those accounted for only 12.8%-13.9% of the variance. Cues relating to family or significant other were chosen by over 90% of patients, and were the most heavily weighted. This study demonstrates that the SEIQoL-DW is of great value in identifying those factors which contribute to the psychosocial well-being of an individual with ALS. However, SEIQoL index scores may not reflect aggregate QoL of groups of patients with ALS, and may be measuring a construct other than QoL. Caution should be exercised in using the SEIQoL index score to measure QoL of groups, such as would be needed in interventional trials.
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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 | 0.046 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".