Cancer symptom burden negatively affects health-related quality of life in patients undergoing prehabilitation prior to liver resection: results from a 12-week randomized controlled trial
Bibliographic record
Abstract
This study aimed to (i) explore determinants of health-related quality of life (HRQoL) in patients with cancer awaiting liver resection and entering a prehabilitation program, and (ii) examine the effect of prehabilitation on HRQoL in both the pre- and postoperative period. We randomized patients to prehabilitation or rehabilitation. Prehabilitation began an exercise, nutrition, and relaxation intervention 4 weeks preoperatively; rehabilitation began the same intervention postoperatively. We measured the following at baseline, preoperatively, 4 weeks and 8 weeks postoperatively: HRQoL [Functional Assessment of Cancer Therapy-General (FACT-G)], nutritional status [abridged Patient-Generated Subjective Global Assessment (aPG-SGA)], cancer symptom burden [revised Edmonton Symptom Assessment System (ESAS-r)], fatigue [Brief Fatigue Inventory (BFI)] and anxiety/depression [Hospital Anxiety and Depression Scale (HADS)], 6 min walking distance, handgrip strength, and body composition. At baseline ( n = 35, prehabilitation = 17), there were significant negative associations between FACT-G and ESAS-r total score ( r = −0.675, p < 0.001), HADS depression ( r = −0.618, p < 0.001), BFI ( r = −0.612, p < 0.001), aPG-SGA ( r = −0.432, p < 0.05), and HADS anxiety ( r = −0.397, p < 0.05). There were no associations between FACT-G and strength/function tests or body composition. Robust multivariate regression analysis revealed ESAS-r was the only variable to consistently remain significant and predictive of baseline FACT-G ( β = −0.67 to −0.83, p < 0.05, R 2 = 36%–41%). There were no differences in FACT-G within or between groups at any timepoint. Cancer symptom burden was predictive of poor HRQoL in patients entering a prehabilitation program prior to liver resection. Future prehabilitation studies in this patient population should test whether the addition of supportive care measures to relieve cancer-related symptoms will lead to significant improvements in HRQoL. Clinicaltrials.gov registration NCT03475966. Take home message Cancer symptoms negatively affect health-related quality of life (HRQoL) in patients with cancer awaiting liver resection. Prehabilitation maintained HRQoL after surgery. Future studies should test whether relieving cancer symptoms can improve HRQoL.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".