The Risk of Malnutrition and its Impact on Quality of Life in Head and Neck Cancer
Bibliographic record
Abstract
Abstract Objectives Individuals with head and neck cancer (HNCa) are at an increased risk of malnutrition. Therefore, the use of self-administered, outpatient nutrition screens that seek information specific to nutritional concerns may yield benefits of identifying a need for intervention which may facilitate improved treatment outcomes and quality of life (QOL). Methods In this descriptive, cohort case series conducted at a tertiary care center, fifty participants (36 men, 14 women) completed one demographic and two QOL surveys, a nutrition assessment, two nutrition self-screening tools, and an ease-of-use questionnaire. Results obtained from nutrition screens were compared to those of the nutrition assessment. Additionally, the relationships between QOL, nutrition status, and demographics were examined. Results Thirty-two percent of participants were identified as nutritionally compromised. In this patient population, the sensitivity and specificity for the PG-SGA SF and Pt-Global Application were found to be 81.25% and 100%, and 68.76% and 100%, respectively. Additionally, alterations in nutrition status were associated with QOL. Conclusions Data suggest that self-administered nutrition screens may be a viable option which enable proactive identification of nutritional concerns associated with HNCa.
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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 | 0.000 |
| 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) | 0.002 | 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".