A Brief Measure of Life Participation for People with COPD: Validation of the Computer Adaptive Test Version of the Late Life Disability Instrument
Bibliographic record
Abstract
Computer-adaptive tests use respondents’ answers to previous questions to select the subsequent questions. They are gaining popularity for their increased measurement precision and decreased administration time compared to static questionnaires. The purpose of this study was to estimate the test-retest reliability and construct validity of the computer-adaptive test version of a participation measure, the Late Life Disability Instrument (LLDI-CAT) for people with COPD and to compare scores and administration time with those of the static LLDI. Among 76 older adults with COPD, scores on the LLDI-CAT were compared to scores on measures of related constructs, between groups based on symptom severity, prognosis and frailty phenotype, and to scores on the static LLDI. A subsample of 28 people completed the LLDI-CAT a second time within one week of the initial administration for test-retest reliability. The LLDI-CAT had very good test-retest reliability (ICC2,1 0.88; SEM 2.74 points), fair correlations with physical function (r = 0.37-0.50), anxiety (r=-0.42), and depression (r=-0.50), fair to moderately-strong correlations with quality of life (r = 0.48-0.63), and strong correlation with the static LLDI limitation domain (r = 0.80). The LLDI-CAT scores differed between people with different symptom severity, prognosis and frailty phenotype (p ≤ 0.004). The mean administration time for the LLDI-CAT was 3.3 (1.5) minutes, less than that of the static LLDI at 6.3 (2.8) minutes (p < 0.001). The LLDI-CAT demonstrates evidence of test-retest reliability and construct validity, and correlates well with the limitation domain of the static LLDI for people with COPD. The LLDI-CAT can be used to assess participation for this population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".