Inflammatory markers correlate with lymphocytes infiltrating and predict immunotherapy prognosis for esophageal cancer
Bibliographic record
Abstract
Aim: To investigate the prognostic value of inflammatory markers in esophageal squamous cell carcinoma (ESCC) patients treated with immune checkpoint inhibitors (ICIs).Materials & methods: The infiltration of CD3+ and CD8+ T cells in tissue microarrays from 180 patients who underwent radical esophagectomy was detected using immunohistochemistry. A separate cohort of 351 patients with metastatic/recurrent or unresectable ESCC treated with ICIs was enrolled for further investigation. The overall survival difference among groups was assessed using Kaplan–Meier analysis. Cox proportional hazards models were employed to investigate the prognostic impact of the inflammatory markers, along with other factors.Results: Decreased inflammation was found to be associated with increased CD3+ and CD8+ T-cell infiltration and a better prognosis. Then, the value of inflammatory markers in predicting survival in 351 ESCC patients receiving immunotherapy was validated. Ultimately, the systemic immune-inflammation index was identified as an independent prognostic factor for overall survival. Additionally, the patients with no distant organ metastasis, or treated by first-line immunotherapy combined with concurrent chemoradiotherapy can considerably prolong survival.Conclusion: Inflammation is associated with the level of tumor infiltrating lymphocytes and that the systemic immune-inflammation index is an effective prognostic predictor for ESCC patients treated with ICIs.
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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 | 0.000 |
| 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) | 0.001 | 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".