Abstract P1-01-18: Survey of the Current Practice of Sentinel Lymph Node Biopsy in Latin America
Bibliographic record
Abstract
Abstract Background: Despite the fact that breast cancer cases in Latin America (LA) are frequently diagnosed in advanced stages, a significant number of cases are detected early on. Sentinel lymph node biopsy (SLNB) is now the standard of care for nodal staging in early breast cancer and it should be offered to patients even in countries with limited resources. Currently there is limited information regarding practice of SLNB is in LA. The purpose of this study is to explore how SLNB is offered in LA to breast cancer patients when properly indicated, and to investigate the possible obstacles to its implementation. This information would allow us to assess the need for education strategies to expand its use and maintain quality. Methods: An original electronic survey questionnaire was developed to assess self-reported practice in SLNB of all surgeons involved in the management of breast cancer in LA. Moderate to high volume practice was defined as 4 or more breast cancer cases per month or when it comprised more than 25% of the practice. Questionnaires were sent out by e-mail to surgeon members of each of the national surgical associations and/or national mastology societies. For this initial report we selected the following countries: Mexico, Guatemala, Nicaragua, Colombia, Venezuela, Peru, and Uruguay. We present a descriptive analysis of these responses. Results: 330 surgeons responded, of whom 218 (66.1%) were general surgeons and 112 (33.9%) had a subspecialty in breast surgery or surgical oncology. Only 31.8% (105/330) of surgeons reporting to treat breast cancer had a moderate to high caseload. In cities with less than 500,000 inhabitants, breast cancer cases were mainly treated by low volume general surgeons. In larger cities, there were a greater number of specialized surgeons; still a significant number of low volume surgeons are taking care of breast cancer cases. Out of 105 surgeons with moderate to high volume, 93 (88.6%) routinely perform SLNB; of whom more than half (53.8%) perform 5 or more SLNB per month. Nonetheless, 45.2% stated that less than 25% of their caseload was eligible to undergo SLNB. The majority (69.6%) reported using the combined technique (dye and radiotracer). Most (67.9%) learned SLNB technique with a mentor or during fellowship training. When asked about limiting factors to the implementation: 61% highlighted the lack of resources such as gamma probe, nuclear medicine facilities and increased costs as a restrictive factor, and 32.4% pointed out the lack of training opportunities as their main limitation. Just 32 surgeons were involved in SLNB teaching in post-graduate medical training but only 12 of them referred to teach an appropriate number of cases to residents. Conclusion: A significant number of the surgeons treating breast cancer are general surgeons with a low volume of cases but it amounts to a significant number of patients. SLNB is currently been done by surgeons with a moderate to high caseload and/or a sub-specialty. Barriers to implementation appear to be related to scarce resources, lack of training opportunities and low volume of eligible cases. There seems to be a need for increase use of SLNB in LA. Novel strategies to train surgeons and guarantee the quality of their practice are warranted. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P1-01-18.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".