Innovative Approaches to Non-Metastatic Anal Cancer: Bridging Today and Tomorrow
Bibliographic record
Abstract
Anal cancer, although rare, has seen an increasing incidence and mortality, primarily due to high-risk sexual behaviors, HIV, and low HPV vaccination coverage. This review examines current treatment strategies for non-metastatic squamous cell carcinoma of the anus, with a focus on chemoradiation therapy (CRT) and emerging therapies. The historical and scientific basis for chemoradiation therapy using 5-fluorouracil and mitomycin C (MMC) is discussed as the standard treatment, although alternatives such as cisplatin and capecitabine show promise, particularly in settings where MMC is unavailable or when access to infusion pumps is restricted. Negative data regarding treatment intensification, induction or maintenance chemotherapy, and combinations with targeted therapies that have not demonstrated significant benefits are also reviewed. Ongoing research on immune checkpoint inhibitors presents new opportunities to improve patient outcomes. Surgical interventions may be recommended for very early disease but are usually reserved for cases of recurrence or failure after CRT. Despite challenges related to immunization efforts and high-risk behaviors, advancements in CRT and the development of novel therapies offer hope for improved outcomes with reduced toxicity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".