Number of Sentinel Lymph Nodes Removed in Breast Cancer Patients – A Real-World Experience
Bibliographic record
Abstract
Background: Retrieval of less than three Sentinel lymph nodes (SLN) has been shown to be associated with decrease disease specific survival. We aimed to find if the real-world experience replicates the data. Methods: 529 patients with breast cancer (BC) who underwent sentinel lymph node biopsy (SLNB) from Jan 2010 to Dec 2014 were retrospectively reviewed. Data was analyzed using SAS 9.4 software. Chi square test was used if body mass index (BMI) influences the number of SLN retrieved and to detect difference between the variables of using blue dye and radioisotopes for detecting SLN. Results: Proportion of one, two and three or more SLN retrieval was 21, 35 and 44% respectively with the median number two. There was no difference in the number of lymph nodes retrieved if the radioisotope (RI) was used alone or in combination with blue dye (BD). P value of 0. 88. No change in median number of SLN retrieved in different quadrants of the breast was noted. We obtained body mass index (BMI) in 454 patients. The rate of more than two SLN retrieval in patients with normal BMI was 16%. In overweight 12% and obese 18%. We compared SLN= (one, two) vs. SLN= two + group which are crossed tabbed against three BMI categories of normal, overweight and obese. This was statistically significant, p-value of 0. 028. Conclusion: The real-world data suggest sub-optimal retrieval of number of median SLN compared to clinical trials. Higher BMI was associated with less than three SLN retrieved.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".