MétaCan
Menu
Back to cohort
Record W2767487124 · doi:10.1002/ajim.22789

Sinonasal cancer in the Italian national surveillance system: Epidemiology, occupation, and public health implications

2017· article· en· W2767487124 on OpenAlexaff
Alessandra Binazzi, Marisa Corfiati, Davide Di Marzio, Anna Maria Cacciatore, Jana Zajacovà, Carolina Mensi, Paolo Galli, Lucia Miligi, Roberto Calisti, Elisa Romeo, Alessandro Franchi, Alessandro Marinaccio

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsMedicineEpidemiologyEnvironmental healthEtiologyIncidence (geometry)Occupational exposureOccupational diseaseOccupational medicinePublic healthPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Sinonasal cancer (SNC) is a rare tumor with predominant occupational etiology associated with exposures to specific carcinogens. The aim of this study is to describe SNC cases recorded in Italy in the period 2000-2016. METHODS: Clinical information, occupational history, and lifestyle habits of SNC cases collected in the Italian Sinonasal Cancer Register were examined. Age-standardized rates were estimated. RESULTS: Overall, 1529 cases were recorded. The age-standardized incidence rates per 100 000 person-years were 0.65 in men and 0.26 in women. Occupational exposures were predominant among the attributed exposure settings, primarily to wood and leather dusts. Other putative causal agents included chrome, solvents, tannins, formaldehyde, textile dusts, and pesticides. Many cases had unknown exposure. CONCLUSIONS: Epidemiological surveillance of SNC cases and their occupational history is fundamental for monitoring the occurrence of the disease in exposed workers in industrial sectors generally not considered at risk of SNC as well as in non-occupational settings.

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.237
GPT teacher head0.459
Teacher spread0.222 · 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.

Study designObservational
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

Citations46
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Industrial MedicineSame topicHead and Neck Surgical OncologyFrench-language works237,207