Analysis of Factors Influencing the Prognosis of Neoadjuvant Chemotherapy for Breast Cancer with Peripheral Blood Inflammatory Markers
Bibliographic record
Abstract
To explore the factors influencing the prognosis of neoadjuvant chemotherapy for breast cancer. From January 2014 to January 2017 123 breast cancer patients receiving neoadjuvant chemotherapy (NAC) were selected, establish the optimal threshold value of peripheral blood inflammation indicators, patients were divided into high and low groups according to the optimal threshold, contrast different peripheral blood inflammation indexes with pathological complete remission (pCR) after chemotherapy, relationship between disease-free survival (DFS). Patients were divided into two groups with high and low ratios according to the critical values of neutrophil/lymphocyte ratio (NLR) 2.34 and PLR critical value 130.21, the pCR rate of patients with low NLR and PLR group was significantly higher than that of patients with high NLR and high PLR group ( P =0.001), patients with high NLR had shorter DFS than those with low NLR ( P =0.001), patients with high PLR had shorter DFS than those with low PLR ( P =0.001). In patients without pCR after NAC, the DFS of patients in the high NLR group was worse than that in the low NLR group, DFS of patients in the high NLR group were worse than those in the low NLR group, DFS of patients with high PLR group was also worse than that of patients with low PLR group (All P <0.05). Multivariate analysis showed that high PLR and KI-67 were the factors that affected the poor prognosis of breast cancer patients who had received NAC, high NLR was not an independent prognostic factor. High levels of NLR and PLR in peripheral blood before NAC predict poor prognosis in breast cancer patients, Ki-67 and PLR are independent risk factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".