Closing in on Anal Cancer: Has the Era of Immune Checkpoint Inhibitors Truly Arrived?
Bibliographic record
Abstract
Squamous cell carcinoma of the anus (SCCA) is a rare malignancy, with rising incidence and mortality largely attributed to human papillomavirus (HPV) infection. While chemoradiotherapy remains the standard treatment for locoregional disease, the management of recurrent or metastatic SCCA has long been a challenge due to limited therapeutic options and poor prognosis. Recent advancements, particularly the use of immune checkpoint inhibitors (ICIs), have revolutionized the treatment landscape. It has been demonstrated that combining carboplatin-paclitaxel with immunotherapy improves progression-free survival (PFS) and overall survival (OS) in metastatic SCCA, representing a practice-changing development. HPV-positive tumors, with their distinct immunogenic profile, have shown promise in responding to ICIs, although the risk of increased toxicity remains. Current research continues to explore novel treatment combinations, including ICIs, targeted therapies, and chemoradiotherapy, to enhance treatment outcomes and overcome resistance mechanisms. As ICIs gain a more central role in the treatment of SCCA, ongoing trials and future studies will define the optimal strategies for improving patient outcomes in both early and advanced disease.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".