Bibliographic record
Abstract
Abstract Workplace exposures can have a big impact on cancer disparities. Perhaps the earliest lens through which disparities were examined was through the UK “Registrar-General’s Social Classes,” which were introduced in 1913. These six classes were based on occupation ranging from “professional occupations” to “unskilled occupations” and were predictive of mortality. One can think of these occupational groups representing both education and income, but they also represent power, control, social prestige/status, and mobility, as well as differential exposure to a wide range of workplace carcinogens. While nonoccupational behavioral risk factors could potentially confound the relationship between social class based on occupation and cancer, adjustment for these factors often does not remove associations. For example, in IARC’s Synergy project, a pooled analysis of lung cancer case-control studies, strong associations between Treiman’s Standard International Occupational Prestige Scale and International Socio-Economic Index of Occupational Status remained after adjustment for smoking. In studies of occupational cancer, our focus has most often been on “skilled manual” and “semi-skilled” occupations, because it has been easier to document exposure and their employment by either large companies or unionization facilitates recruitment. Although potentially carcinogenic exposures can occur among a wide range of occupations, the risk is not spread evenly. For example, exposure to common workplace lung carcinogens such as asbestos, diesel exhaust, crystalline silica, nickel, and chromium remains prevalent among workers in construction, transportation, mining, and certain manufacturing and service industries, and very rare among managerial, professional, and sales workers. Although the first occupational chemical carcinogen identified was soot, to which chimney sweeps were exposed as child workers, few studies have focused on the lowest, most vulnerable social class because of difficulties recruiting and other practical challenges. For example, studies of pesticides and cancer have most often recruited farmers and pesticide applicators, and not the more vulnerable farm workers, particularly migrant workers. Other examples of vulnerable groups with potentially carcinogenic exposures are electronic waste, nail salon, and cleaning workers. Further studies of cancer among these vulnerable groups are needed. Citation Format: Paul A. Demers. Impact of workplace exposures on cancer disparities [abstract]. In: Proceedings of the AACR Special Conference on Environmental Carcinogenesis: Potential Pathway to Cancer Prevention; 2019 Jun 22-24; Charlotte, NC. Philadelphia (PA): AACR; Can Prev Res 2020;13(7 Suppl): Abstract nr IA22.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".