The association between malnutrition risk and revised Edmonton Symptom Assessment System (ESAS-r) scores in an adult outpatient oncology population: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Cancer-associated malnutrition is associated with worse symptom severity, functional status, quality of life, and overall survival. Malnutrition in cancer patients is often under-recognized and undertreated, emphasizing the need for standardized pathways for nutritional management in this population. The objectives of this study were to (1) investigate the relationship between malnutrition risk and self-reported symptom severity scores in an adult oncology outpatient population and (2) to identify whether a secondary screening tool for malnutrition risk (abPG-SGA) should be recommended for patients with a specific ESAS-r cut-off score or group of ESAS-r cut-off scores. METHODS: A single-institution retrospective cross-sectional study was conducted. Malnutrition risk was measured using the Abridged Patient-Generated Subjective Global Assessment (abPG-SGA). Cancer symptom severity was measured using the Revised Edmonton Symptom Assessment System (ESAS-r). In accordance with standard institutional practice, patients completed both tools at first consult at the cancer centre. Adult patients who completed the ESAS-r and abPG-SGA on the same day between February 2017 and January 2020 were included. Spearman's correlation, Mann Whitney U tests, receiver operating characteristic curves, and binary logistic regression models were used for statistical analyses. RESULTS: 2071 oncology outpatients met inclusion criteria (mean age 65.7), of which 33.6% were identified to be at risk for malnutrition. For all ESAS-r parameters (pain, tiredness, drowsiness, nausea, lack of appetite, shortness of breath, depression, anxiety, and wellbeing), patients at risk for malnutrition had significantly higher scores (P < 0.001). All ESAS-r parameters were positively correlated with abPG-SGA score (P < 0.01). The ESAS-r parameters that best predicted malnutrition risk status were total ESAS-r score, lack of appetite, tiredness, and wellbeing (area under the curve = 0.824, 0.812, 0.764, 0.761 respectively). Lack of appetite score ≥ 1 demonstrated a sensitivity of 77.4% and specificity of 77.0%. Combining lack of appetite score ≥ 1 with total ESAS score > 14 yielded a sensitivity of 87.9% and specificity of 62.8%. CONCLUSION: Malnutrition risk as measured by the abPG-SGA and symptom severity scores as measured by the ESAS-r are positively and significantly correlated. Given the widespread use of the ESAS-r in cancer care, utilizing specific ESAS-r cut-offs to trigger malnutrition screening could be a viable way to identify cancer patients at risk for malnutrition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".