587 PATIENT-REPORTED SYMPTOMS FOR ESOPHAGEAL CANCER PATIENTS UNDERGOING CURATIVE INTENT TREATMENT
Bibliographic record
Abstract
Abstract Esophageal cancer (EC) patients experience considerable symptom burden from treatment. This study utilized population-level patient-reported Edmonton Symptom Assessment System (ESAS) scores collected as part of standard clinical care to describe symptom trajectories and characteristics associated with severe symptoms for patients undergoing curative intent EC treatment. Methods EC patients treated with curative intent at regional cancer centers and affiliates between 2009–2016 and assessed for symptoms in the 12 months following diagnosis were included. ESAS measures nine common patient-reported cancer symptoms. The outcome was reporting of severe (≥7/10) symptom scores. Multivariable analyses were used to identify characteristics associated with severe symptom scores. Results 1,751 patients reported a median of 7 (IQR 4–12) ESAS assessments in the year following diagnosis, for a total of 14,953 unique ESAS assessments included in the analysis. The most frequently reported severe symptoms were lack of appetite (n = 918, 52%), tiredness (n = 787, 45%) and poor wellbeing (713, 40.7%). The highest symptom burden is within the first five months following diagnosis, with moderate improvement in symptom burden in the second half of the first year. Characteristics associated with severe scores for all symptoms included female sex, high comorbidity, lower socioeconomic status, urban residence, and symptom assessment temporally close to diagnosis. Conclusion This study demonstrates a high symptom burden for EC patients undergoing curative intent therapy. Targeted treatment of common severe symptoms, and increased support for patients at risk for severe symptoms, may enhance patient quality of life.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".