Circulating interleukin‐6 level is a prognostic marker for survival in advanced nonsmall cell lung cancer patients treated with chemotherapy
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide as well as in Taiwan. Interleukin-6 (IL-6) is a multifunctional cytokine and has been implicated in tumor progression. This study recruited 245 patients with advanced (Stage 3B/4) nonsmall cell lung cancer (NSCLC) that had received chemotherapy, to evaluate associations between IL-6 and lung cancer-specific survival. Among these subjects, 112 gave blood samples before and 133 after the start of chemotherapy. Plasma IL-6 was measured using an enzyme linked-immunosorbent assay. The 33rd and 66th percentiles of IL-6 concentrations were 2.01 and 25.16 for the 245 patients and were defined as the cutoff points for dividing the patients into low, intermediate and high groups. Kaplan-Meier and Cox proportional-hazard models were used to evaluate the relationship between the IL-6 level and survival time. Results after adjusting for age, sex, smoking history, histologic type and stage of lung cancer revealed a significant relationship. For all patients, the hazard ratio with high IL-6 levels for lung cancer-specific survival was 2.10 [95% confidence interval (CI) = 1.49 - 2.96] compared with low IL-6 levels. The hazard ratio for patients who were recruited before and after the start of chemotherapy was1.25 (95% CI = 0.73 - 2.13) and 3.66 (95% CI = 2.18 - 6.15), respectively. Patients with high circulating IL-6 also responded poorly to chemotherapy. Therefore, a high level of circulating IL-6 was associated with an inferior response and survival outcome in NSCLC patients treated with chemotherapy.
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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 | 0.002 |
| 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".