Quality of life assessment in esophagectomy patients
Bibliographic record
Abstract
Esophagectomy is the mainstay of curative therapy for esophageal cancer; however, it is associated with significant morbidity and mortality, with subsequent major impact on quality of life. This paper reviews the evaluation of health-related quality of life (HRQOL) in esophageal cancer patients undergoing curative intent therapy, the relationship between postoperative HRQOL and survival as well the potential utility of pre-treatment HRQOL as a prognostic tool. HRQOL assessment is valuable in helping clinicians understand the impact on patients of esophageal cancer and the various treatments thereof. HRQOL is also valuable as an end-point in studies of esophageal cancer and esophageal cancer treatment. Given the morbidity and mortality associated with the various treatments for esophageal cancer, it could be argued that HRQOL is as important an endpoint as survival, if not more so. Patient-reported pre-treatment HRQOL assessment appears to predict survival better than clinician-derived performance status assessment period. HRQOL assessment also appears to be responsive to surgical and non-surgical therapy and thus could potentially be used in trials and in practice to serve that function. Thus, HRQOL assessment could be a potentially important adjunct in shared decision-making and guiding treatment planning as well as monitoring the progress of treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".