Validation of the functional assessment of cancer therapy esophageal cancer subscale
Bibliographic record
Abstract
BACKGROUND: To develop and validate a quality of life subscale for patients with esophageal cancer to be used with the Functional Assessment of Cancer Therapy-General (FACT-G). METHODS: Prospective cohort study of patients with esophageal cancer treated with surgery alone or neoadjuvant chemoradiotherapy and surgery evaluating the validity, internal consistency, and responsiveness to change of the FACT-Esophageal (FACT-E) when comparing it with the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ 30) and esophageal (OES 24) as well as clinical factors. RESULTS: The FACT-E demonstrated very good convergent and divergent validity when compared with the EORTC QLQ30 and OES 24 and clinical variables. Internal consistency was also good with coefficient alpha > 0.70 for all subscales and individual items. Stability coefficients were > 0.80. Changes in clinical status were reflected in changes in FACT-E scores demonstrating responsiveness to change, particularly in patients receiving neoadjuvant chemoradiotherapy before surgery. CONCLUSIONS: The FACT-E met or exceeded all standards for validity, providing an option to measure health-related quality of life for different treatment strategies for esophageal cancer.
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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 | 0.007 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".