Assessing Patterns of Practice of Sentinel Lymph Node Biopsy for Breast Cancer in Latin America
Bibliographic record
Abstract
INTRODUCTION: There is a lack information regarding how sentinel lymph node biopsy (SLNB) for breast cancer is carried out today in developing countries and how it was adapted. To rectify this situation we performed a pattern-of-practice survey amongst practicing surgeons in Latin America (LA). METHODS: A survey was developed to assess current surgical practice in breast cancer, use of SLNB, limitations to the implementation, training, technique variations, and observed adverse events. A total of 30 surgical associations and breast surgery societies in 18 Latin American countries were invited to participate. Surveys were distributed among member of these associations and 76.7 % of those contacted answered the survey. Responses were limited only to those who reported treating breast cancer patients. RESULTS: A total of 463 surgeons who manage breast cancer responded. Over 53 % of surgeons do not have sub-specialty training. Only 47.7 % have a high-volume case load, of which 87.8 % routinely perform SLNB. The main limitations perceived to the implementation of SLNB were a lack of resources/equipment (48 %) and training opportunities (33 %). Over 60 % reported that fewer than half of their patients were eligible for SLNB and 67.8 % reported that they were involved in teaching this technique to residents. CONCLUSIONS: A significant proportion of surgeons that treat breast cancer cases in LA have not had sub-specialty training or manage a low volume of cases. Among those surgeons with a high-volume caseload, SLNB is routinely performed. SLNB training during residency represents an opportunity for improvement in the region.
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 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".