Prognostic role of geriatric nutritional risk index (GNRI) and controlling nutritional status (CONUT) on outcomes in patients with head and neck cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Malnutrition is a common comorbidity in patients with head and neck cancer (HNC), significantly impacting survival rates. The Geriatric Nutritional Risk Index (GNRI) and the Controlling Nutritional Status (CONUT) score are tools used to assess the nutritional status, yet their prognostic value in HNC remains to be fully established. METHODS: We performed a systematic review and meta-analysis, adhering to PRISMA guidelines, to evaluate the prognostic significance of GNRI and CONUT on survival outcomes in patients with HNC. Relevant studies up to March 2024 were identified through comprehensive searches of PubMed, EMBASE, and Cochrane CENTRAL databases. The quality of each included study was assessed using the Newcastle-Ottawa Scale. RESULTS: Seventeen studies were included, encompassing a total of 3,816 patients with HNC. Our findings reveal that a lower GNRI is consistently associated with poor overall survival (OS, adjusted hazard ratio [aHR]: 3.9, 95% confidence interval [CI]: 2.47-6.14) and progression-free survival (PFS, aHR: 1.76, 95% CI: 1.41-2.21), demonstrating its utility as a prognostic indicator. However, CONUT scores revealed no significant differences in OS (aHR: 1.65, 95% CI: 0.94-2.91) or PFS (aHR: 1.43, 95% CI: 0.68-3.02). CONCLUSION: GNRI appears to be a reliable prognostic tool for predicting poorer survival outcomes in HNC patients, underscoring the importance of nutritional assessments in this population. Further research is needed to clarify the prognostic value of the CONUT score, which exhibited less consistent results.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".