Sarcopenia and postoperative morbidity in head & neck cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To identify the prognostic value of sarcopenia in surgically treated HNC patients on postoperative morbidity. DESIGN: Systematic review and meta-analysis. INFORMATION SOURCES, STUDY SELECTION, AND METHODS: EMBASE, MEDLINE, SCOPUS, and CINAHL databases were searched from January 1, 1946 to November 4, 2024. Published trials and observational studies reporting the association of sarcopenia and postoperative complications in surgically treated HNC patients were included. Two reviewers independently screened, extracted, and appraised studies using Covidence. Disagreements were resolved through consensus, and/or by consulting a third reviewer. Data were pooled using a random-effects model in RevMan 5.4.1. RESULTS: Of 6345 screened studies, 17 out of the 23 included studies had outcomes which were incorporated in the meta-analysis (2884 patients from studies between 1996 and 2024). The meta-analysis revealed a statistically significant association between sarcopenia and all postoperative complications (odds ratio (OR) 2.26, 95 % CI [1.54, 3.33], p < 0.0001), postoperative complications grade 3 (OR 2.34, 95 % CI [1.80, 3.03], p < 0.00001), fistula (OR 2.64, 95 % CI [1.68, 4.16], p < 0.0001), and flap complications (OR 2.77, 95 % CI [1.58, 4.85], p = 0.0004). Subgroup analysis revealed the high risk of bias studies did not significantly bias the results (p = 0.76), but the different measurements of sarcopenia contributed significant heterogeneity (p < 0.00001). The level of evidence is moderate, primarily due to publication bias, for all outcomes, as per GRADE. CONCLUSIONS: In patients undergoing curative surgery for HNC, preoperative sarcopenia is associated with higher odds of postoperative complications.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.048 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".