Multimodal cancer treatment and its association with nutrition care practices in patients with head and neck and esophageal cancer: an international prospective cohort study
Bibliographic record
Abstract
Background: Both cancer and its’ treatment contribute to the development of malnutrition, particularly in cancers that impact nutrition intake such as head and neck (HNC) and esophageal (EC) cancers. This study was undertaken to explore the relationship between cancer treatment and nutrition care in patients with HNC and EC. Methods: Adult patients (≥18 years) with newly diagnosed head and neck (HN) or esophageal (ESO) cancers scheduled to receive cancer treatment were enrolled between 2016 and 2018 in the INFORM study, a longitudinal multi-centre prospective cohort study. Baseline clinical characteristics of patients, cancer characteristics, treatment type (chemotherapy/radiotherap y /surgery) and frequency, nutrition risk (Patient Generated Subjective Global Assessment Short Form (PG-SGA SF) and nutrition care were recorded. Results: 100 HNC and 51 EC patients were included. Data were collected across 4 time periods from baseline to 6 months at 11 sites in Canada, Australia Italy, The Netherlands and the United States. Seventy-nine percent of the patients were male with a mean (SD) age of 63 (10) years. At admission, the mean (SD) BMI was 27 (5) kg/m2 and 30% were current smokers. Baseline PGA-SGA SF was ≥ 4 indicating nutrition risk for 59% of the HNC and 77% of the EC patients. The number of cancer treatments was positively associated with increases in enteral (EN) and parenteral nutrition (PN). In HNC patients receiving a single cancer treatment, 39% required EN and with 3 cancer treatment types, 78% required EN. In EC requiring a single cancer treatment, 50% required EN and in patients with 3 cancer treatments 94% required EN. Conclusion: The number of cancer treatment modalities is associated with the intensity of nutrition therapy required to sustain the patients through their cancer journey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".