Bibliographic record
Abstract
Skin cancer is the most common type of neoplasm worldwide. Skin cancer can be classified as non-melanoma skin cancer (NMSC) or melanoma skin cancer (MSC). NMSCs are more common lesions, and typically carry a good prognosis. MSCs are rarer, but cause the majority of skin cancer-related death and morbidity. The biggest causative factor for any skin cancer is ultraviolet radiation exposure (UVR). UVR comes from the sun or synthetic sources such as tanning beds, making it highly avoidable through behavioral change. Despite this, the incidence of skin cancer in Canada has risen over the past decades at an alarming rate. There are many levels of preventive medicine currently in place attempting to change this trend. Primary strategies include wearing hats and protective clothing, as well as avoiding peak daytime hours and tanning beds. At a secondary level, clinical skin exams and public education work to identify disease at earlier stages to make treatments more effective. Both surgical and destructive management can be effective, but success is highly dependent on the stage of disease. Even with appropriate treatment there is a risk of complications including deformity, recurrence and even death. This emphasizes the best treatment for skin cancer is prevention and further underscores the need for behavioural changes at the population level.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.097 | 0.020 |
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".