Bibliographic record
Abstract
Melanoma incidence is increasing constantly worldwide in recent years: 132,000 melanoma skin cancers occur globally each year (WHO-INTERSUN). Despite this, no adequate evidence regarding the role of cumulative solar UV exposure in inducing the skin cancer has been provided. Recently, some studies appear to indicate that, also in patients with melanoma history, the habit of completely avoiding sun exposure is not a positive prognostic factor. According to IARC monograph published in 2012, evidences regarding UV risk factors for melanoma are the intermittent UV exposure with recurrent sunburns, especially in childhood and adolescence. According to these findings, various studies on occupational exposure to solar radiation (SR) failed to find an association between the performance of an outdoor job and the risk of melanoma. Recently, in Italy melanoma due to SR exposure has been erased from the national list of occupational diseases (D.P.R. 1124/65, last modification in 2014). But, in Europe an occupational health surveillance is needed for workers exposed to Artificial UV radiation according to EU Directive 2006/25/CE, and a skin examination for these workers is suggested, but quite paradoxically there are not similar indications for workers exposed to natural UV radiation. Considering the great number of outdoor workers employed in Europe, at least 14 million according to OSHA, and worldwide, the consideration of occupational solar radiation exposure as a specific professional risk requiring the health surveillance of exposed workers will be very helpful in order to prevent melanoma and other UV related diseases.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".