Association of Level III Axillary Lymph Node Positivity with Clinicopathological Characteristics in Breast Cancer
Bibliographic record
Abstract
Management of breast cancer has gradually shifted from era of radical surgery to present days of multi-modality management and conservatism. While complete axillary dissection is common for node-positive cases, less invasive approaches like sentinel node biopsy are often sufficient for clinically node-negative cases. However, these findings may not apply to all populations, particularly in India where advanced disease presentation is common. The objective of this study is to assess Level III Axillary Lymph Node Positivity with clinicopathological characteristics in Breast cancer. This was a hospital based retrospective observational study on breast cancer patients conducted in single institute from 2016 to 2022. A total of 70 patients with operable breast cancers, who underwent primary tumour resection and complete axillary lymph node dissection, including level III were included in the study. Patients with inoperable and metastatic disease were excluded. Final histopathological examination data was collected and analysed. Most patients (92.9%) underwent Modified Radical Mastectomy, with Infiltrating Ductal Carcinoma (IDC) being the most common histology (90%). Factors significantly associated with level III lymph node positivity included tumour size >4.5cm, nuclear grade III, pathological N3 stage and extra nodal extension. The study found no significant correlation with other factors like age, tumour laterality, location, hormone receptor status, HER2 status, or LVSI. These findings may help predict level III lymph node involvement in breast cancer patients. All these predictive factors should be considered during the axillary dissection.
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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 | 0.002 |
| 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) | 0.002 | 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".