Sinonasal cancer in the Italian national surveillance system: Epidemiology, occupation, and public health implications
Bibliographic record
Abstract
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".