The Predictive Value of Pre-treatment Inflammatory Markers in Advanced Non-small-Cell Lung Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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