Geriatric Nutritional Risk Index Predicts Treatment Intolerance and Survival in Head and Neck Cancer
Bibliographic record
Abstract
BACKGROUND: Malnutrition is associated with worse outcomes in head and neck cancer (HNC). The geriatric nutritional risk index (GNRI) may predict postoperative morbidity and survival, but its role remains underexplored. METHODS: An ambispective study of patients undergoing HNC surgery at two academic centers (2015-2024) was performed. Preoperative GNRI was categorized into moderate-to-high risk (< 92), low risk (92-98), and no risk (> 98). Outcomes included 90-day mortality, treatment intolerance, overall survival, and disease-free survival. Analyses were performed using multivariable logistic and Cox regression adjusted for age, Charlson Comorbidity Index (CCI), tumor stage, primary tumor site, and percutaneous endoscopic gastrostomy (PEG) status. Patients with prior head and neck cancer or prior radiation were excluded. RESULTS: Among 312 treatment-naïve surgical patients, 13% were moderate-to-high GNRI risk and 8% low risk. Moderate-to-high GNRI risk had higher major adverse events (57% vs. 36% in no-risk), greater treatment intolerance (61% vs. 41%), and a trend toward higher 90-day mortality (11% vs. 4%). On multivariable models adjusted for tumor site and pathologic stage, moderate-to-high GNRI risk was associated with higher odds of 90-day mortality (OR 2.99, 95% CI 1.01-9.31; p = 0.048) and treatment intolerance (OR 2.35, 95% CI 1.21-4.56; p = 0.012). Cox regression showed shorter overall (HR 2.28, 95% CI 1.44-3.62; p < 0.001) and disease-free survival (HR 1.87, 95% CI 1.20-2.91; p = 0.005). CONCLUSIONS: The GNRI predicted treatment intolerance and poorer survival in patients with operable HNC.
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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".