Bibliographic record
Abstract
The incidence and mortality of squamous cell carcinoma of the anus has been gradually increasing globally over the last few decades. The evolution of different modalities, including immunotherapies, has changed the treatment paradigm of metastatic anal cancers. Chemotherapy, radiation therapy, and immune-modulating therapies form the backbone of treatment of anal cancer in various stages. Most anal cancers are linked to high-risk human papilloma virus (HPV) infections. HPV oncoproteins E6 and E7 are responsible for an anti-tumor immune response triggering the recruitment of tumor-infiltrating lymphocytes. This has led to the development and utilization of immunotherapy in anal cancers. Current research in anal cancer is moving forward to discover ways to incorporate immunotherapy in the treatment sequencing in various stages of anal cancers. Immune checkpoint inhibitors alone or in combination, adoptive cell therapy, and vaccines are the areas of active investigations in anal cancer in both locally advanced and metastatic settings. Immunomodulating properties of non-immunotherapies are incorporated to enhance immune checkpoint inhibitors' effectiveness in some of the clinical trials. The aim of this review is to summarize the potential role of immunotherapy in anal squamous cell cancers and future directions.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".