Association between Temporal Muscle Thickness and Overall Survival in Non-Small Cell Lung Cancer Patients with Brain Metastasis
Bibliographic record
Abstract
Temporal muscle thickness (TMT) has recently been suggested as a novel biomarker of sarcopenia in head and neck malignancies. However, few studies have evaluated TMT as a prognostic marker in patients with brain metastasis. This study investigated the association of TMT with overall survival (OS) in non-small cell lung cancer (NSCLC) patients with brain metastasis. The records of all NSCLC patients with brain metastasis between 2009 and 2018 at St. Vincent’s Hospital were reviewed retrospectively. A total of 221 patients met our eligibility criteria. In the group with TMT thicker than the median, OS was longer than the group with TMT thinner than the median (240 days versus 139 days, p = 0.014). In multivariate analysis, the thicker TMT group had longer survival (HR 0.73 CI 0.56−0.96, p = 0.024). Male (HR 1.58 CI 1.19−2.09, p = 0.002) and older age (≥65 years) (HR 2.05 CI 1.53−2.74, p < 0.001) also showed statistical significance. We also performed subgroup analysis in older patients (≥65 years). In this subgroup of 107 patients, the thicker TMT group also showed longer OS than the thinner TMT group (209 days versus 82 days, p = 0.009). Our findings suggest that TMT can be a useful biomarker for OS in NSCLC patients with brain metastasis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".