Elevated Platelet-to-Lymphocyte Ratio and Neutrophil-to-Lymphocyte Ratio after First Cycle of Chemotherapy and Better Survival in Esophageal Cancer Patients Receiving Concurrent Chemoradiotherapy
Bibliographic record
Abstract
Background: Prognostic factors for poor survival have been proposed in esophageal squamous cell carcinoma (SCC) patients receiving concurrent chemoradiotherapy (CCRT). We conducted a retrospective study on hematological profile after first cycle of chemotherapy for esophageal SCC patients receiving CCRT. Methods: From January 2008 to December 2017, a total of 420 patients with esophageal SCC were enrolled. All included patients had undergone CCRT. Complete blood count, differential count, NLR, and PLR before chemotherapy (CHT) and after first cycle of CHT were obtained. Univariate and multivariate Cox regression analyses were used to assess the association between survival and patient, disease, and treatment characteristics. Results: On univariate analysis, significant factors for overall survival (OS) and disease specific survival (DSS) included ECOG performance status, clinical staging, operation, cisplatin dose, prechemotherapy NLR and PLR, and elevated postchemotherapy NLR. On multivariate analysis, ECOG performance status 0–I, Clinical staging I–II, Operation, cisplatin dose >150 mg/m2, prechemotherapy PLR <375, and postchemotherapy platelet count ≥150 × 109/L were independent factors for predicting better OS. Independent factors for predicting better DSS included ECOG performance status 0–I, Clinical staging I–II, Operation, cisplatin dose >150 mg/m2, and prechemotherapy PLR <375. Conclusions: Our study showed that low levels of prechemotherapy PLR and NLR were associated with better OS and DSS. Elevated platelet count and NLR after first cycle of CHT were associated with better OS. Elevated PLR and NLR after first cycle of CHT were associated with better DSS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".