Assessing dietary intake in accordance with guidelines: Useful correlations with an ingesta-Verbal/Visual Analogue Scale in medical oncology patients
Bibliographic record
Abstract
Background & aims Energy intake and food ingesta are central in nutritional screening and assessment. Cancer patients are at nutritional risk of losing weight, and clinicians need quick and easy tools to identify patients for nutritional support. This study aimed to evaluate the feasibility and the accuracy of a Visual/Verbal Analogue Scale of food ingesta ( ingesta -VVAS) to assess energy food intake and nutritional risk in medical oncology patients. Methods Dieticians administered prospectively the ingesta -VVAS in 1762 medical oncology patients . The external validity of the ingesta- VVAS was determined against daily energy intake based on a 24-h dietary recall. Patients had to estimate how they currently ate on a scale from 0 " nothing at all " to 10 " as usual ". Area Under the Receiver-Operating Characteristics (ROC) curve served as determine the optimal cut-off and provide the discriminative power of the tool to detect patients who ingested less or more than 25 kcal kg −1 day −1 . Results The feasibility of the ingesta -VVAS was 97.7%. The scores were significantly correlated with energy intake ( ρ = .67, p < .05), whatever the specific situation ( i.e. malnutrition or not). With a cut-off of ≤7, the ingesta -VVAS exhibited a good power discrimination (AUC = .804) to detect patients who ingested less or more than 25 kcal kg −1 day −1 , with a sensitivity of 80.8%, a positive predictive value of 83.6%, a specificity of 67.5%, and a negative predictive value of 63.3%. Patients with a score ≤7 on the ingesta -VVAS score were at 12-fold higher probability of nutritional risk [OR 12.3; 95% CI (8.7–17.4); p < .001]. Sensitivity to detect patients with a significant weight loss was 71%, and a positive predictive value of 75.9%. Conclusions This easy-to-use ingesta- VVAS is well-correlated with energy intake and may be useful in clinical practice. An ingesta- VVAS score is ≤ 7 could be used to detect patients with nutritional risk of weight loss in medical oncology.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".