Baseline Insulin-Like Growth Factor-I Plasma Levels, Systemic Inflammation, Weight Loss and Clinical Outcome in Metastatic Non-Small Cell Lung Cancer Patients
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Bibliographic record
Abstract
BACKGROUND: Cancer patients frequently suffer from weight loss and systemic inflammation in the context of advanced disease, which is related to adverse outcome. Insulin-like growth factor (IGF)-I is an anabolic molecule implicated in the maintenance of muscle mass and cancer growth. We investigated potential correlations of IGF-I with an inflammatory and weight loss status and with clinical outcome. METHODS: Baseline IGF-I plasma levels were measured in 77 patients (66 males, median age 65.5 ± 10.6 years), diagnosed with metastatic non-small cell lung cancer, and were correlated with serum albumin and C-reactive protein (CRP) levels, weight loss history, treatment response and overall survival. RESULTS: IGF-I correlated with age (p = 0.01), histologic subtype (p = 0.019), albumin (p < 0.001) and CRP (p < 0.001). In univariate analysis, gender (p = 0.005), smoking status (p = 0.012), albumin (p = 0.034) and IGF-I (p = 0.017) were related to time to progression, while IGF-I (p = 0.003), gender (p = 0.049) and smoking status (p = 0.003) retained their significance in multivariate analysis. Age (p = 0.005), gender (p = 0.029), weight loss (p = 0.009), performance status (p < 0.001), number of metastatic sites (p = 0.004), albumin (p = 0.008), CRP (p = 0.022) and IGF-I (p = 0.042) were associated with overall survival, although only gender (p = 0.013), weight loss (p = 0.027), performance status (p = 0.015) and number of metastatic sites (p = 0.021) emerged as independent prognostic factors. CONCLUSION: IGF-I correlates with systemic inflammation and seems to play an independent predictive role in metastatic non-small cell lung cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it