Editorial Current Treatments for Skin Cancer
Bibliographic record
Abstract
Skin cancer is a common cancer affecting a large population of Caucasians around the world. The best treatmentresult can only be achieved by a multidisciplinary team to evaluate all host, tumor and treatment factors carefully ina particular case scenario.The thematic issue includes overviews of non-melanoma skin cancers, melanoma and Merkel cell carcinoma.The pattern of care review reflects how skin cancer is cared for in North America, such as Canada and in Europe,such as France. The paper on treatment decisions illustrates how to approach a case and make decision for treatment.The review on the role of different specialties in the team gives a broad coverage particularly from the surgicalperspective. Reviews on radiotherapy, photodynamic therapy and systemic therapy are excellent, covering latestresearch results and conference abstracts. The whole issue can be used at the point of care by selecting the subtopics.Readers will have enough information by reading the subtopics without going through the whole review to getthe most relevant advice.Health care professionals in practice and in training will find this issue helpful at the point of care. It is potentiallyuseful for preparation of professional examinations since almost all boards or colleges for specialties testcompetence on skin cancer management. At the point of care, this issue contains clinical pearls and experience fromdifferent authors. It will also form the basis of local guideline development.Without the dedicated contribution of all authors and reviewers, this issue would not be possible. We also wouldlike to thank our patients who inspire us to pursue more knowledge in this common cancer.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.027 |
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".