Nutritional Support in Cancer patients: update of the Italian Intersociety Working Group practical recommendations
Bibliographic record
Abstract
Malnutrition is a frequent problem in cancer patients, which leads to prolonged and repeated hospitalizations, increased treatment-related toxicity, reduced response to cancer treatment, impaired quality of life, a worse overall prognosis and the avoidable waste of health care resources. Despite being perceived as a limiting factor in oncologic treatments by both oncologists and patients, there is still a considerable gap between need and actual delivery of nutrition care, and attitudes still vary considerably among health care professionals. In the last 5 years, the Italian Intersociety Working Group for Nutritional Support in Cancer Patients (WG), has repeatedly revisited this issue and has concluded that some improvement in nutritional care in Italy has occurred, at least with regard to awareness and institutional activities. In the same period, new international guidelines for the management of malnutrition and cachexia have been released. Despite these valuable initiatives, effective structural strategies and concrete actions aimed at facing the challenging issues of nutritional care in oncology are still needed, requiring the active participation of scientific societies and health authorities. As a continuation of the WG's work, we have reviewed available data present in the literature from January 2016 to September 2021, together with the most recent guidelines issued by scientific societies and health authorities, thus providing an update of the 2016 WG practical recommendations, with suggestions for new areas/issues for possible improvement and implementation.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".