Multidisciplinary Management of Locally Advanced and Metastatic Cutaneous Squamous Cell Carcinoma
Bibliographic record
Abstract
Non-melanoma skin cancers are the most prevalent form of cancer, with cutaneous squamous cell carcinoma (cscc) being the 2nd most common type. Patients presenting with high-risk lesions associated with locally advanced or metastatic cscc face high rates of recurrence and mortality. Accurate staging and risk stratification for patients can be challenging because no system is universally accepted, and no Canadian guidelines currently exist. Patients with advanced cscc are often deemed ineligible for either or both of curative surgery and radiation therapy (rt) and, until recently, were limited to off-label systemic cisplatin-fluorouracil or cetuximab therapy, which offers modest clinical benefits and potentially severe toxicity. A new systemic therapy, cemiplimab, has been approved for the treatment of locally advanced and metastatic cscc. In the present review, we provide recommendations for patient classification and staging based on current guidelines, direction for determining patient eligibility for surgery and rt, and an overview of the available systemic treatment options for advanced cscc and of the benefits of a multidisciplinary approach to patient management.
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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".