A multidisciplinary rehabilitation programme for cancer cachexia improves quality of life
Bibliographic record
Abstract
OBJECTIVES: Patients with cancer cachexia have severely impaired quality of life (QoL). Multidisciplinary, multimodal treatment approaches have potential for stabilising weight and correcting other features of this syndrome, but the impact on QoL is unknown. METHODS: A retrospective analysis of QoL in patients with advanced cancer, referred for the management of cachexia by a specialised multidisciplinary clinic (The McGill Cancer Nutrition Rehabilitation Program clinic at the Jewish General Hospital (CNR-JGH)). QoL was assessed at visits 1-3 using a dedicated QoL tool for cachexia, and the change in QoL was calculated for each patient. The correlation between clinical features and QoL at baseline and subsequent change in QoL was analysed, to determine what factors predict improvements in QoL during the CNR-JGH intervention. RESULTS: 374 patients assessed at visit 1 with mean weight loss of 10.2% over the preceding 6 months. Baseline QoL scores were severely impaired but clinically important improvements were observed over visits 1-3 to the CNR-JGH clinic. Improvements in QoL were not determined by baseline characteristics and were similar in all patient subgroups. However, those patients who gained weight and increased their 6 min walk test (6MWT) had the greatest improvements in QoL. CONCLUSIONS: Improving management of all facets of the cancer cachexia syndrome, including poor QoL, remains a priority. The multimodal approach to management of cancer cachexia offered by the CNR-JGH results in clinically important improvements in QoL. All patients who are able to receive this type of intervention have similar potential to improve their QoL, but the greatest benefits are seen in those who gain weight and improve their 6MWT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".