Health-related quality of life measure distinguishes between low and high clinical T stages in esophageal cancer
Bibliographic record
Abstract
BACKGROUND: Functional Assessment of Cancer Therapy-Esophagus (FACT-E) is a health-related quality of life (HRQOL) instrument validated in patients with esophageal cancer. It is made up of both a general component and an esophageal cancer subscale (ECS). Our objective was to explore the relationship between baseline FACT-E, ECS and clinically determined T-stage in patients with stage II-IV cancer of the gastroesophageal junction or thoracic esophagus. METHODS: Data from four prospective studies in Canadian academic hospitals were combined. These were consecutive and eligible patients treated between 1996 and 2014 with clinical stage II-IV cancer of the gastroesophageal junction or thoracic esophagus. All patients completed pre-treatment FACT-E. Parametric (ANOVA) and non-parametric (Kruskal-Wallis) analyses were performed. RESULTS: Of the 135 patients that were deemed eligible, the T-stage distribution determined clinically was: 10 (7.4%) T1, 33 (24.4%) T2, 79 (58.5%) T3 and 13 (9.6%) T4. Parametric analysis showed no significant association between FACT-E & T-stage, although there was a trend towards significance (P=0.08). Non-parametric analysis showed a significant association between FACT-E and T-stage (P=0.05). Post-hoc tests identified that the most significant differences in FACT-E scores were between T1 and T3 patients. Both parametric (P=0.002) and non-parametric (P=0.003) analyses showed an association between ECS & T-stage. Post-hoc analyses showed significant differences in ECS scores between T1 and higher T-stages (P<0.01). CONCLUSIONS: Patient-reported HRQOL scores appear to be significantly different in patients with clinical T1 esophageal cancer as compared to those with higher clinical T stages. Since distinguishing T1 from T2/T3 lesions is important in guiding the most appropriate treatment modality and since EUS appears to have difficulties reliably making such T-stage distinctions, FACT-E and ECS scores may be helpful as an adjunct to guide decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".