Baseline Insulin-Like Growth Factor-I Plasma Levels, Systemic Inflammation, Weight Loss and Clinical Outcome in Metastatic Non-Small Cell Lung Cancer Patients
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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".