A Measure of Disease-Specific Health-Related Quality of Life for Achalasia
Bibliographic record
Abstract
OBJECTIVES: To develop a measure of disease-specific health-related quality of life for achalasia for use as an outcome measure in clinical trials. METHODS: We generated a list of potential items for a measure of disease-specific health-related quality of life for achalasia by semistructured interviews with seven persons with achalasia, and by expert opinion. We then used factor analysis and item response theory methods for item reduction, using responses on the long-form questionnaire from 70 persons with achalasia. The severity measure underlying the item responses was constructed using a Rasch model. RESULTS: We developed a 10-item measure of disease-specific health-related quality of life that sampled the concepts of food tolerance, dysphagia-related behavior modifications, pain, heartburn, distress, lifestyle limitation, and satisfaction. The measure was reliable (person separation reliability 0.79, Cronbach's alpha 0.83), showed evidence of construct validity and good data-to-model fit (mean infit and outfit statistics for items, 1.00 and 0.98, respectively), and had a wide effective measurement range (able to discriminate between 87% of subjects with achalasia). The measure was recalibrated onto a 0-100 interval-level scale. CONCLUSIONS: We describe a reliable measure of achalasia disease-specific health-related quality of life that has a broad effective measurement range, interval-level properties, and evidence of construct validity. This measure is appropriate for use as an outcome measure in clinical trials and other evaluative studies on the effectiveness of treatment for achalasia.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".