The association of nutritional assessment criteria with health-related quality of life in patients with advanced colorectal carcinoma
Bibliographic record
Abstract
Health-related quality of life (QoL) is a goal in nutritional oncology but the association between nutritional status and QoL is rarely explored. The aim of the study was to investigate the association of nutritional assessment criteria with QoL in 50 patients with advanced colorectal carcinoma. A second aim was to investigate changes in body weight and QoL during a 3-month follow-up. Muscle mass, nutritional risk, malnutrition and cachexia according to three different criteria were assessed, as well as health-related QoL. At inclusion, 36 patients experienced weight loss, 10 patients sarcopenia, 25 were at nutritional risk, 16 were malnourished and 11, 14 and 31 patients had cachexia according to different criteria. All nutritional assessment criteria discriminated between groups of patients with worse or better QoL to varying degrees. Malnutrition and cachexia defined by the European Palliative Care Research Collaborative and adjusted for recent gain or stabilisation of body weight discriminated on most QoL scores. Weight loss at follow-up was associated with a decrease in several QoL scores. Recognition of weight loss as well as diagnosing malnutrition and cachexia should be the first steps in an interventional pathway to enhance nutritional status and QoL in patients with advanced colorectal carcinoma.
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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 | 0.000 |
| 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 | 0.001 | 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".