Clinical practice guidelines for the care and treatment of breast cancer: 13. Sentinel lymph node biopsy.
Bibliographic record
Abstract
OBJECTIVE: To provide information and recommendations to women with breast cancer and their physicians regarding what is now known about sentinel lymph node (SLN) biopsy. OPTIONS: Axillary dissection; SLN biopsy followed by backup axillary dissection; SLN biopsy. OUTCOMES: Accurate determination of cancer stage, resulting in better-informed therapeutic decisions. EVIDENCE: Systematic review of English-language literature published from January 1991 to December 2000 retrieved primarily from MEDLINE and CANCERLIT. RECOMMENDATIONS: Axillary dissection is the standard of care for the surgical staging of operable breast cancer. If a patient requests or is offered SLN biopsy, the benefits and risks as well as what is and is not known about the procedure should be outlined. Patients should be informed of the number of SLN biopsies performed by the surgeon and the surgeon's success rate with the procedure, as determined by the identification of the SLN and the false-negative rate (the presence of tumour cells in the axillary nodes when the SLN biopsy result is negative). Before surgeons replace axillary dissection by SLN biopsy as the staging procedure at their institution, they should (a) familiarize themselves with the literature on the topic and the techniques needed to perform the procedure, (b) follow a defined protocol for all 3 aspects of the procedure (nuclear medicine, surgery, pathology) and (c) perform backup axillary dissection until an acceptable success rate (as determined by the identification of the SLN and the false-negative rate) is achieved. A surgeon who performs breast cancer surgery infrequently should not perform SLN biopsy. A positive SLN biopsy result or failure to identify an SLN should prompt full axillary dissection. SLN biopsy is contraindicated in women who have clinically palpable nodes, locally advanced breast cancer, multifocal tumours, previous breast surgery or previous irradiation of the breast. Staining of tissue sections with hematoxylin and eosin, and not immunohistochemical analysis for cytokeratin, should determine adjuvant therapy. Participation in randomized clinical trials is encouraged. [A patient version of these guidelines appears in Appendix 1.] VALIDATION: Internal validation within the Steering Committee on Clinical Practice Guidelines for the Care and Treatment of Breast Cancer; no external validation.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.032 |
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".