Practice patterns of lymph-node mapping and sentinel-node biopsy for breast cancer in British Columbia.
Bibliographic record
Abstract
INTRODUCTION: Because there is no standardized technique for mapping of lymph nodes and no optimal technique for evaluating the sentinel node, we decided to evaluate practice patterns for sentinel-node biopsy (SNB) for breast cancer in British Columbia 5 years after its introduction in 1996. METHODS: We carried out mail and telephone surveys of general surgeons performing at least 1 SNB (n = 28) or not performing SNB (n = 50), and carried out telephone surveys or on-site visits with pathologists (n = 7) and nuclear medicine physicians (n = 5) from institutions supporting SNB in the province. We collected data on training, perceived indications and techniques for the surgical, imaging and pathologic assessments of SNB to obtain data on practice patterns in 2001 and the degree of consistency among surgeons and institutions involved in performing SNB and reasons for not adopting the SNB technique. RESULTS: By 2001, SNB was incorporated into the practice of 19% of surgeons (28 of 150) performing breast cancer surgery in British Columbia. The survey response rate among SNB surgeons was 89% (25 of 28). Twelve (48%) of the 25 surgeons implemented SNB in the context of a validation study. Ten (40%) of the 25 had no data management support to monitor their results. Surgical training included intraoperative mentoring alone (48%), formal training courses alone (20%), both (24%) and self-teaching (8%). One-third of the surgeons had performed fewer than 10 procedures. Five surgeons had abandoned routine axillary dissection. There was considerable variation regarding the indications for SNB, definition of a sentinel node and surgical techniques. All nuclear medicine departments had a written lymphatic mapping protocol, but each used a different volume and activity of radiotracer. Immunohistochemical evaluation of the sentinel nodes was performed at just 3 pathology laboratories. The survey response rate from surgeons not practising SNB was 54% (27 of 50). Among 24 responders in active practice, 7 (29%) planned to perform SNB; 79% had not decided on the SNB indications. Lack of operating room time was a major limiting factor. CONCLUSIONS: There was considerable variation in the surgical, nuclear medicine and pathology techniques for SNB in the absence of a planned approach for its implementation in British Columbia. Developing consensus around written guidelines for the indications and techniques of SNB may reduce this variation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".