Accuracy of fine-needle aspiration cytology of axillary lymph nodes in breast cancer patients
Bibliographic record
Abstract
BACKGROUND: Fine-needle aspiration (FNA) cytology of axillary lymph nodes is a simple, minimally invasive technique that can be used to improve preoperative determination of the status of the axillary lymph nodes in patients with breast cancer, thereby serving as a tool with which to triage patients for sentinel versus full lymph node dissection procedures. The aim of the current study was to determine the sensitivity and specificity of FNA cytology to detect metastatic breast carcinoma in axillary lymph nodes. METHODS: A total of 115 FNAs of axillary lymph nodes of breast cancer patients with histologic follow-up (subsequent sentinel or full lymph node dissection) were included in the current study. The specificity and sensitivity, as well as the positive and negative predictive values, were calculated. RESULTS: The positive and negative predictive values of FNA cytology of axillary lymph nodes for metastatic breast carcinoma were 1.00 and 0.60, respectively. The overall sensitivity of axillary lymph node FNA in all the cases studied was 65% and the specificity was 100%. The sensitivity of FNA was lower in the sentinel lymph node group than in the full lymph node dissection group (16% vs 88%, respectively), which was believed to be attributable to the small size of the metastatic foci in the sentinel lymph node group (median, 0.25 cm). All false-negative FNAs, with the exception of 1 case, were believed to be the result of sampling error. There was no 'true' false-positive FNA case in the current study. CONCLUSIONS: FNA of axillary lymph nodes is a sensitive and very specific method with which to detect metastasis in breast cancer patients. Because of its excellent positive predictive value, full axillary lymph node dissection can be planned safely instead of a sentinel lymph node dissection when a preoperative positive FNA result is rendered. .
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".