MétaCan
Menu
Back to cohort
Record W3036125252 · doi:10.1093/annweh/wxaa039

The OMEGA-NET International Inventory of Occupational Cohorts

2020· article· en· W3036125252 on OpenAlexaff
Manolis Kogevinas, Vivi Schlünssen, Ingrid Sivesind Mehlum, Michelle C. Turner

Bibliographic record

VenueAnnals of Work Exposures and Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Ottawa
FundersGeneralitat de CatalunyaEuropean Cooperation in Science and TechnologyEuropean Social FundCentres de Recerca de CatalunyaVlaamse regeringMinisterio de Ciencia, Innovación y Universidades
KeywordsOmegaEnvironmental healthMedicinePhysics

Abstract

fetched live from OpenAlex

In a recent count of cohort studies in Europe capturing information on occupation and/or occupational exposures, we estimated that there are more than 60 major studies with some type of occupational information that enrolled over 30 million persons. With few exceptions there have been no large-scale analyses systematically combining cohorts from this extraordinary resource. We present the development of an inventory of cohorts with occupational information in Europe and internationally and describe the online interactive tool with detailed information on existing cohorts. The OMEGA-NET inventory can be accessed at http://occupationalcohorts.net/ includes cohorts, case-control studies nested within cohorts and intervention studies that are active or can substantiate that their data are potentially accessible; that include data on occupation and/or industry or at least one occupational exposure; and that have at least one follow-up, either already conducted or planned. We expect that this open access inventory will be an important prerequisite for use of this resource of existing studies for research and policy development.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.163
GPT teacher head0.460
Teacher spread0.297 · 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 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

Citations16
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of Work Exposures and HealthSame topicWorkplace Health and Well-beingFrench-language works237,207