The relationship between health utility, quality of life, and symptom scores in Canadian patients with esophageal cancer.
Bibliographic record
Abstract
149 Background: Health Utility scores (HUS) are an increasingly important tool in helping to determine the cost-effectiveness of therapies worldwide. The EQ-5D is a validated HUS questionnaire, with reference data in numerous populations. Previously, HUS in esophageal cancer (EC) were based on limited datasets, and the relationship between HUS and either quality of life (QOL, through the validated FACT-E) or esophageal-specific symptoms such as dysphagia, has not been studied. Methods: This cross-sectional survey of EC patients at Princess Margaret Cancer Centre (2012-2014) assessed EQ-5D, FACT-E, a Visual Analog Scale (VAS), patient reported performance status (PRO-ECOG), and dysphagia scoring. EQ-5D scores were converted to HUS using Canadian references. Correlation analyses were performed between HUS and global FACT-E scores, global dysphagia scores, and specific esophageal symptom scores included in FACT-E. Results: Of 198 patients, median age was 67 (range 32-93) years, 76% were male, with localized (LD stage 1, 6%), regional (RD, stage II-IVA, 62%), and metastatic (MD, stage IVB, 27%) disease. Mean + SEM EQ-5D HUS was 0.80+0.01 (all patients), 0.90+0.05 (LD), 0.82+0.01 (RD), and 0.73+0.03 (MD) [p=0.03]. Mean FACT-E total score was 130, mean total FACT-G score was 80, and mean ECS score was 49. There was a strong correlation between FACT-E total scores and EQ-5D HUS (r=0.73, p<0.001), and mild-to-moderate correlation between FACT-E dysphagia questions and HUS (r= 0.28-0.37; p<0.001, each comparison) and between the odynophagia question and HUS (r=0.28, p<0.001). A moderate correlation was observed between a non-FACT-E based global swallow score and HUS (r=0.48, p<0.001). Conclusions: In this large cross-sectional study of EC patients, stage, QOL, and esophageal-specific symptoms were all associated with HUS. Additional results will be presented on the relationship of VAS, PRO-ECOG and specific FACT-E domains, with HUS and changes in questionnaire scores over time, as well as stage-specific EC reference HUS using UK and USA references. This research enhances our understanding of the factors driving EQ5D HUS in EC, thereby validating its potential usefulness in economic analyses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".