Bibliographic record
Abstract
PURPOSE OF REVIEW: To highlight the recent treatment recommendations that are proposed to improve the outcomes of patients with advanced penile cancer. RECENT FINDINGS: Penile cancer is a rare disease that presents with many diagnostic and treatment specificities. Adequate treatment requires interdisciplinary discussions involving urologist oncologists, medical oncologists and radiation oncologists as well as many other health professionals. Localized disease can now be adequately treated with many different penile-sparing approaches associated with close surveillance. However, many patients present with high-risk features associated with an unfavorable outcome. Recent data and multiple interdisciplinary collaborative groups have fueled a more standardized approach to the treatment of these patients, leading to better survival outcomes and improved quality of life. This review will focus on the recognition of high-risk disease; the indications of lymph node dissections and minimally invasive surgical approaches; the role of neoadjuvant therapy in the setting of advanced lymph node involvement as well as the potential role of targeted therapy for the salvage treatment of metastatic disease. SUMMARY: Improved outcomes in advanced penile cancer requires an integrated approach, including better identification of high-risk disease, use of novel dynamic sentinel lymph node identification techniques and use of neo-adjuvant chemotherapy in selected patients with advanced lymph node involvement. Such cases should preferentially be managed by a dedicated interdisciplinary team.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".