Quality of life following cytoreductive surgery and hyperthermic intraperitoneal chemotherapy: a Canadian experience
Bibliographic record
Abstract
<h3>Background:</h3> Cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) is an effective treatment for select peritoneal surface malignancies, offering a survival benefit. However, the procedure can substantially impact patients’ quality of life (QoL) owing to its physiologic and functional effects. We sought to evaluate QoL trajectories over 12 months following CRS/HIPEC and to identify factors influencing outcomes. <h3>Methods:</h3> A prospective cohort study was performed including patients undergoing CRS/HIPEC between January 2018 and July 2022. Quality of life was assessed preoperatively and at 3, 6, and 12 months postoperatively using the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 and QLQ-CR29 questionnaires. Qualitative data from open-ended responses were analyzed thematically. <h3>Results:</h3> Twenty-five patients were included in the study. Global health scores declined at 3 months (<i>p</i> = 0.024) but returned to near baseline by 12 months. Physical function showed similar recovery, and cognitive function improved significantly by 6 months (<i>p</i> = 0.017). Sexual function showed increased interest (<i>p</i> < 0.001) but worsening symptoms. Key QoL predictors included age, sex, postoperative complications, systemic therapy, and recurrence. Family support, medical care, and active lifestyles positively influenced recovery, whereas prognosis concerns and physical limitations were negative factors. <h3>Conclusion:</h3> Quality of life after CRS/HIPEC showed dynamic recovery over 12 months and was influenced by clinical, demographic, and psychosocial factors, emphasizing the need for comprehensive perioperative support.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".