Preoperative Quality-of-Life and Recovery Following Esophagectomy: Identifying Modifiable Factors Associated with Improvement
Bibliographic record
Abstract
BACKGROUND Esophagectomy remains the primary curative treatment for esophageal cancer, but it often leads to substantial declines in health-related quality of life (HRQoL). While prior studies have identified various demographic and clinical factors associated with postoperative HRQoL, most are non-modifiable and offer limited opportunities for intervention. This study aimed to examine whether modifiable preoperative factors, particularly baseline HRQoL, are associated with clinically meaningful improvement in HRQoL one year after esophagectomy. METHODS This retrospective cohort study used data from the McGill Esophageal and Gastric Database. Patients were included if they underwent curative esophagectomy for esophageal cancer and completed the Functional Assessment of Cancer Therapy-Esophageal (FACT-E) both before surgery and at one year postoperatively. HRQoL improvement was defined as a ≥ 5-point increase in total FACT-E score. Stepwise multivariable logistic regression was used to evaluate the association between preoperative factors and HRQoL improvement, with additional sensitivity analyses across various improvement thresholds. RESULTS Of 108 eligible patients, 42% experienced clinically meaningful HRQoL improvement one year after surgery. Lower preoperative FACT-E scores were significantly associated with greater odds of improvement. No other modifiable factors, including BMI, smoking, or alcohol use, were significantly associated with improvement. Sensitivity analyses confirmed consistent findings across alternative HRQoL thresholds. Among demographic variables, female sex was associated with lower odds of improvement. CONCLUSION Patients with greater preoperative symptom burden were more likely to experience meaningful improvements in HRQoL after esophagectomy. These findings support the value of routine preoperative HRQoL assessment and suggest that baseline HRQoL could be used to identify patients who may benefit from targeted prehabilitation or psychosocial support. As precision medicine advances, incorporating patient-reported outcomes into preoperative risk stratification could enhance personalized recovery planning and improve survivorship care.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".