The Predictive Value of Pre-treatment Inflammatory Markers in Advanced Non-small-Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Accurate prediction of outcome in advanced non-small-cell lung cancer (NSCLC) remains challenging. Even within the same stage and treatment group, survival and response to treatment vary. We set out to determine the predictive value of inflammatory markers C-reactive protein (CRP) and white blood cells (WBCS) in patients with advanced NSCLC. PATIENTS AND METHODS: Patients were assigned a prognostic index (PI): 0 for crp 10 mg/L or less and WBCS 11x10⁹/L or less, 1 if one of the two markers was elevated, and 2 if both markers were elevated. We then used chest computed tomography (CT) imaging to evaluate response after 2 cycles of chemotherapy treatment. RESULTS: Of 134 patients, 46 had a PI of 0; 60, a PI of 1; and 28, a PI of 2. Disease progressed in 41 patients. Progression was significantly more frequent among patients with a PI of 2 (p = 0.008). Median survival was 20.0 months for the PI 0 group, 10.4 months for the PI 1 group, and 7.9 months for the PI 2 group (p < 0.001). The PI was the only significant prognostic factor for survival even after adjustment for performance status, smoking, and weight loss (hazard ratio: 1.57; 95% confidence interval: 1.2 to 2.14; p = 0.004). CONCLUSIONS: Inflammatory state correlates significantly with both chemotherapy response and survival in stage IV NSCLC. The PI may provide additional guidance for therapeutic decision-making.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".