Reply to Sentinel Lymph Node in Vulvar Cancer
Bibliographic record
Abstract
We thank Dr. Mahajan for his comments and interest in our article regarding sentinel lymph nodes in vulvar cancer.1 We do agree with Dr. Mahajan that the sentinel lymph node procedure can add a significant cost to the treatment. This increase in cost is both due to the injection of radioactive colloid and the subsequent involvement of nuclear imaging, the equipment (gamma probe), and the added workload created for the pathology department to process and review the sentinel lymph nodes according to the protocol mentioned in our article.1 The needs and issues regarding healthcare in developing countries are vastly different from developed countries. In the former, delivery of basic needs is an issue. Whereas this also is an issue in developed countries, we have the luxury of trying to develop more efficacious treatments and treatments that are designed primarily to reduce toxicity and improve quality of life. Therefore, we agree with Dr. Mahajan that a complete inguinofemoral lymph node dissection is a time-honored method of staging and treating vulvar cancer and should continue to be used until sentinel lymph node biopsy alone is proven efficacious and affordable. Jan Hauspy MD*, Allan Covens MD*, Marlo Beiner MD*, Ian Harley MD*, Lisa Erlich MD*, Golnar Rasty MD*, Jan Hauspy MD , * Division of Gynecologic Oncology, Toronto-Sunnybrook Regional Cancer Center, Toronto, Ontario, Canada, Juravinski Cancer Center-McMaster University, Hamilton, Ontario, Canada.
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 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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.028 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".