Impact of preoperative plasma levels of interleukin 6 and interleukin 6 soluble receptor on disease outcomes after radical cystectomy for bladder cancer
Bibliographic record
Abstract
Preoperative plasma levels of Interleukin 6 (IL6) and its soluble receptor (IL6sR) have previously been associated with oncologic outcomes in urothelial carcinoma of the bladder (UCB); however, external validation in patients treated with radical cystectomy (RC) for UCB is missing.We prospectively collected preoperative plasma from 1,036 consecutive patients at two institutes. These plasma specimens were assessed for levels of IL6 and IL6sR. Logistic and Cox regression analyses were used to assess the correlation of plasma levels with pathologic and survival outcomes. The additional clinical net benefits of preoperative IL6 and IL6sR were evaluated using decision curve analysis (DCA).Median IL6 and IL6sR plasma levels were significantly higher in patients with adverse pathologic features. Elevated biomarker levels were independently associated with an increased risk for lymph node metastasis and ≥ pT3 disease. Both biomarkers were independently associated with recurrence-free survival (RFS), cancer-specific survival (CSS) and overall survival (OS). The addition to, respectively, fitted pre- and postoperative prognostic models improved the predictive accuracy for lymph node metastasis, ≥ pT3 disease, RFS and CSS on DCA.We confirmed that elevated preoperative plasma levels of IL6 and IL6sR levels are associated with worse oncological disease survival in patients treated with RC for UCB in a large multicenter study. Both biomarkers hold potential in identifying patients with adverse pathological features that may benefit from intensified/multimodal therapy and warrant inclusion into predictive/prognostic models. They demonstrated the ability to improve the discriminatory power of such models and thus guide clinical 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".