Refining items for a preference-based, amyotrophic lateral sclerosis specific, health-related quality of life scale
Bibliographic record
Abstract
Objective: The aim of this study was to refine the items of a preference-based amyotrophic lateral sclerosis health-related quality of life scale (PB-ALS HRQL scale) based on domains generated in a previous study. Methods: Survey methodology was used to assess item importance rating (IR) and independence. Median importance was calculated for each item and a rating of “very important” was required for the item to remain. Correlations were used to examine item independence. Highly correlated items (rs > 0.7) were considered for removal. Cognitive debriefing (CD) interviews, conducted by Zoom, telephone, or email based on participant preference and communication needs, were used to identify potential issues. Participants provided feedback about wording, clarity, response options, and recall period on randomly selected items. Items were considered finalized when three sequential CD participants approved the item with no revisions. Results: Thirty-four people with ALS (PALS, n = 16 females; age range 44–78 years; ALS Functional Rating Scale-Revised [ALSFRS-R] range 0–48) in Canada completed the survey; a subset of 18 PALS completed CD interviews (n = 8 female; age range 44–71 years; ALSFRS-R range 0–48). Four items were highly correlated with one or more items, were not rated as very important, or were not approved via CD and were removed. Conclusions: The final four-response option PB-ALS Scale includes eight items: recreation and leisure, mobility, interpersonal interactions and relationships, eating and swallowing, handling objects, communicating, routine activities, and mood. The next step is to translate the PB-ALS Scale into French and develop a scoring algorithm based on PALS' preferences.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".