Keratinocyte Carcinomas: Current Concepts and Future Research Priorities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) are keratinocyte carcinomas, the most frequently diagnosed cancers in fair-skinned populations. Ultraviolet radiation (UVR) is the main driving carcinogen for these tumors, but immunosuppression, pigmentary factors, and aging are also risk factors. Scientific discoveries have improved the understanding of the role of human papillomaviruses (HPV) in cSCC as well as the skin microbiome and a compromised immune system in the development of both cSCC and BCC. Genomic analyses have uncovered genetic risk variants, high-risk susceptibility genes, and somatic events that underlie common pathways important in keratinocyte carcinoma tumorigenesis and tumor characteristics that have enabled development of prediction models for early identification of high-risk individuals. Advances in chemoprevention in high-risk individuals and progress in targeted and immune-based treatment approaches have the potential to decrease the morbidity and mortality associated with these tumors. As the incidence and prevalence of keratinocyte carcinoma continue to increase, strategies for prevention, including effective sun-protective behavior, educational interventions, and reduction of tanning bed access and usage, are essential. Gaps in our knowledge requiring additional research to reduce the high morbidity and costs associated with keratinocyte carcinoma include better understanding of factors leading to more aggressive tumors, the roles of microbiome and HPV infection, prediction of response to therapies including immune checkpoint blockade, and how to tailor both prevention and treatment to individual risk factors and needs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it