A systematic review and meta-analysis to evaluate blood levels of interleukin-6 in lung cancer patients
Bibliographic record
Abstract
Introduction: The exact mechanism responsible for inflammation in malignancy is not completely understood, but it is possible that interleukin-6 (IL-6) plays a major role in triggering and maintaining an inflammatory response. Aim: To conduct a systematic review and meta-analysis of the levels of IL-6 in the serum/plasma of lung cancer (LC) patients. Material and methods: The researchers searched four databases up to September 11, 2022, to find studies that reported on IL-6 levels in LC patients compared to healthy controls (HCs). They calculated effect sizes using standardized mean difference (SMD) with a 95% confidence interval (CI). To evaluate the quality of each study, they used the Newcastle-Ottawa Scale (NOS). They performed subgroup analysis, sensitivity analysis, meta-regression analysis, heterogeneity analyses, trial sequential analysis, and publication bias with the trim-and-fill method. Results: = 98%), indicating that LC patients had significantly higher levels of IL-6 in their serum/plasma than HCs. Conclusions: The study found that the publication year and quality score of the studies were positively associated with the level of IL-6, while the sample size was inversely related. The research suggests that measuring IL-6 levels in the blood could be useful for detecting and monitoring LC as it appears to be a reliable biomarker.
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How this classification was reachedexpand
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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.024 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".