Number of nodes in sentinel lymph node biopsy for breast cancer: Are surgeons still biased?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The purpose of this study was to assess the number of lymph nodes removed at SLNB, and what factors might bias a surgeon's decision to remove additional nodes. METHODS: A prospectively maintained database was reviewed. All patients that had SLNB for primary treatment of breast cancer between January 2012 and March 2016 were identified. Clinicopathologic factors were used to compare the number of LNs and rates of node positivity. RESULTS: One thousand six hundred and three patients were included. The average number of SLNs, non-SLNs, and total LNs was 2.53, 0.54, 3.08, respectively. Significantly more LNs were removed in age <40 versus age >40 (3.73, 3.04 P < 0.01), invasive versus DCIS (3.13, 2.73 P < 0.001), Grade III versus Grade II (3.42, 2.99 P < 0.01), T2 versus T1 (3.40, 2.96 P < 0.01), and ER- versus ER+ (3.45, 3.05 P < 0.05). SLN positivity was significantly higher (P < 0.05) in invasive versus DCIS (27%, 4%), T2 versus T1 (30%. 17%), Grade II versus Grade I (42%, 18%), and ILC versus IDC (38%, 26%). CONCLUSIONS: There was a significant difference in the number of lymph nodes removed at SLNB in certain groups however; node positivity was not necessarily higher in these groups. Surgeons must be cognizant of potential bias when performing SLNB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".