Prognostic significance of pretreatment inflammatory biomarkers in non-metastatic breast cancer
Bibliographic record
Abstract
Background: Recently, peripheral blood inflammatory biomarkers such as neutrophil-lymphocyte ratio (NLR) have been identified for their prognostic role in many types of cancers. Elevated NLR was associated with poor prognosis & increased mortality rates. This study assessed the predictive value of pretreatment NLR in non-metastatic breast cancer.Objective: To assess the role of pretreatment NLR in non-metastatic breast cancer and their effect on prognosis in terms of 5 years disease-free survival and overall survival.Methods: This retrospective cross-sectional study was conducted in Suez Canal University Hospitals in Ismailia, Egypt. 105 patients with pathologically proven breast cancer were recruited from January 2015 to December 2016. Patients & tumor characteristics were collected from medical records. Five-year overall survival & disease-free survival were analyzed.Results: Mean patients’ ages were 47.82 ± 11.65. The age ranges were between 25 & 78 years. There was no statistical significance between patients with low & high pretreatment NLR in terms of patients’ characteristics & tumor variables. With the ROC curve, the cut-off points for NLR were 1.65 & 1.55 for DFS and OS, respectively. In terms of patients’ DFS & OS, no statistically significant difference was found between non-metastatic breast cancer patients with low & high NLR (plog-rank = .357 and .236, respectively). No statistically significant difference was found between patients with low & high pretreatment NLR in the period of five years OS & DFS.Conclusions: Pretreatment NLR is an inflammatory biomarker that might affect patient prognosis and survival. Further research is required to confirm the prognostic significance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".