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Preventing Melanoma with the Help of Occupational Physicians

2016· article· en· W2560599005 on OpenAlexvenueno aff
Alberto Modenese

Bibliographic record

VenueJournal of cancer research updates · 2016
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMelanomaSkin cancerEnvironmental healthSun exposureOccupational exposureIncidence (geometry)CancerDermatologyInternal medicineCancer research

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.426
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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