O3D.7 Constances: a population-based cohort for occupational epidemiology
Bibliographic record
Abstract
The CONSTANCES general-purpose population-based cohort is intended to serve as an epidemiological research infrastructure accessible to the epidemiologic research community. CONSTANCES also provides useful public health information to the public health authorities. CONSTANCES was designed as a randomly selected sample of French adults aged 18–69 years at inception; 2 00 000 subjects will be included over a six-year period. At inclusion, the selected subjects are invited to complete questionnaires, including a lifetime job history, and to attend a Health Screening Centre (HSC) for a comprehensive health examination. A biobank is being set up. The follow-up includes a yearly self-administered questionnaire, and a periodic visit to an HSC. Social and health data are collected from the French national administrative databases. Data collected for participants include social and demographic characteristics, socioeconomic status, life events, and behaviours. Regarding occupational factors, a full job history and a wealth of data on employment and organizational, chemical, biological, biomechanical and psychosocial lifelong exposure are collected at inception and during the follow-up. The health data cover a wide spectrum: self-reported health scales, reported prevalent and incident diseases, long-term chronic diseases and hospitalizations, sick-leaves, handicaps, limitations, disabilities and injuries, healthcare utilization and services provided, and causes of death. To consider non-participation at inclusion and attrition throughout the longitudinal follow-up, a cohort of non-participants was set up and will be followed through the same national databases as participants. Inclusion began in 2012 and more than 1 80 000 participants were enrolled by July 2018. Several projects on occupational risks are already in progress, and an Occupational Health Users Club was established. This platform and its potential contributions will be described, as well as the means for international investigators to access it.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".