Prognostic significance of a positive axillary lymph node fine‐needle aspirate in patients with invasive breast carcinoma
Bibliographic record
Abstract
BACKGROUND: Image-guided axillary lymph node fine-needle aspirates (FNAs) correlate well with pathologic lymph node staging in cases of invasive breast carcinoma. The objective of this study was to determine the prognostic significance of a positive lymph node. METHODS: Consecutive cases of nonmetastatic (M0) invasive breast carcinoma evaluated by image-guided FNA were identified (4-year period, median follow-up of 51 months). "Positive" and "nonpositive" groups were compared using Kaplan-Meier survival analysis. Multivariate Cox regression was used to correct for clinicopathologic and treatment factors. A total of 142 cases was included, 70 with positive axillary FNA and 72 with a nonpositive result. RESULTS: FNA-positive and nonpositive cases did not differ in patient age, tumor subtype, or hormone receptor status. Positive FNA was significantly associated with advanced T and N pathologic stage, and with HER2 (human epidermal growth factor receptor 2) positivity. FNA-positive patients were more likely to undergo mastectomy and to receive chemotherapy. Kaplan-Meier analysis showed that positive FNA is associated with poor prognosis, both with respect to disease-free survival (89% nonpositive versus 73% positive at 5 years, P < .001) and overall survival (94% versus 81%, respectively, at 5 years, P = .01). Multivariate analysis showed that when correcting for other variables, FNA positivity was not independently significant. CONCLUSIONS: Positive axillary lymph node FNA is associated with poor prognosis on univariate analysis. By contrast, overall nodal staging is independently significant on multivariate analysis. The prognostic significance of axillary FNA likely results from its ability to predict for nodal status. Axillary FNA has utility as a preoperative staging procedure.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".