When the Loss Costs Too Much: A Systematic Review and Meta-Analysis of Sarcopenia in Head and Neck Cancer
Bibliographic record
Abstract
Purpose: Whether or not skeletal muscle mass (SMM) depletion, known as sarcopenia, has significant negative effects on the prognosis of patients with head and neck cancer (HNC) is both new and controversial. In this meta-analysis, we aimed to determine the prognostic significance of sarcopenia in HNC. Methods: We searched PubMed, the Cochrane Library, Embase, and Web of Science, which contains trial registries and meeting proceedings, to identify related published or unpublished studies. We used the Newcastle-Ottawa Scale (NOS) to appraise the risk of bias of the included retrospective studies. Pooled hazard ratios (HR) and the I2 statis-tic were estimated for the impact of sarcopenia on overall survival (OS) and relapse-free survival (RFS). Results: We analyzed data from eleven studies involving 2483 patients (39.4% on av-erage of whom had sarcopenia). Based on the univariate analysis data, the sarcopenia group had significantly poorer OS compared to the non-sarcopenia group (HR =1.97, 95% confidence interval [CI]:1.71-2.26, I2 = 0%). In the cut-off value subgroup, group 1, defined as skeletal muscle index (SMI) of 38.5 cm2/m2 for women and 52.4 cm2/m2 for men (HR =2.41, 95% CI: 1.72-3.38, I2 = 0%), had much poorer OS. In the race subgroup, the results were consistent between the Asia (HR = 2.11, 95% CI: 1.59-2.81) and non-Asia group (HR = 1.92, 95% CI: 1.64-2.25). The sarcopenia group also had significantly poorer RFS (HR = 1.74, 95% CI: 1.43-2.12, I2 = 0%). Conclusions: Presence of pre-treatment sarcopenia has a significant negative impact on OS and RFS in HNC compared with its absence. Further well-conducted studies with detailed stratification are needed to complement our findings. Keywords: Head and neck cancer; Sarcopenia; Meta-analysis; Prognostic factor
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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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".