Occupational exposures and fluorescent oxidation products in 723 adults of the EGEA study
Bibliographic record
Abstract
Occupational asthma can be induced by a variety of agents, including high and low molecular weight sensitisers, and respiratory irritants [1]. The role of exposure to cleaning products and disinfectants in work-related asthma is increasingly recognised, although the specific substances that increase asthma risk are not well identified [2]. Some of the numerous agents contained in these products are chemical sensitisers, but most are hypothesised to act as respiratory irritants [2]. While high molecular weight sensitisers are known to cause occupational asthma through a typical allergic response, the pathophysiological mechanisms involved in occupational asthma induced by low molecular weight (LMW) chemicals, and in irritant-induced asthma, remain poorly understood [1, 3, 4]. Associations between occupational exposures to asthmogenic chemicals and irritants and oxidative stress were found <http://ow.ly/K6RSt> The authors thank all those who participated in the study and in the various aspects of the examinations and all those who supervised the study centres. The authors are grateful to the three CIC-Inserm units at Necker, Grenoble and Marseille (France), which supported the study and where subjects were examined. They are also grateful to the three biobanks in Lille (CIC Inserm), Evry (Centre National de Genotypage) and Annemasse (Etablissement Français du Sang; France) where biological samples are stored. The authors thank Sylwester Karpiel (INSERM U1018, Centre for research in Epidemiology and Population Health (CESP), Respiratory and Environmental Epidemiology Team, Villejuif, France) for his contribution to this work. They are indebted to all the individuals who participated, without whom the study would not have been possible. The EGEA cooperative group are as follows. Coordination: V. Siroux (epidemiology, PI since 2013); F. Demenais (genetics); I. Pin (clinical aspects); R. Nadif (biology); F. Kauffmann (PI 1992–2012). Respiratory epidemiology: Inserm U 700, Paris: M. Korobaeff (Egea1) and F. Neukirch (Egea1); Inserm U 707, Paris: I. Annesi-Maesano (Egea1–2); Inserm CESP/U 1018, Villejuif: F. Kauffmann, N. Le Moual, R. Nadif, MP. Oryszczyn (Egea1–2) and R. Varraso; Inserm U 823, Grenoble: V. Siroux. Genetics: Inserm U 393, Paris: J. Feingold; Inserm U 946, Paris: E. Bouzigon, F. Demenais and M.H. Dizier; CNG, Evry: I. Gut (now CNAG, Barcelona, Spain) and M. Lathrop (now McGill University, Montreal, Canada). Clinical centres: Grenoble: I. Pin and C. Pison; Lyon: D. Ecochard (Egea1), F. Gormand and Y. Pacheco; Marseille: D. Charpin (Egea1) and D. Vervloet (Egea1–2); Montpellier: J. Bousquet; Paris Cochin: A. Lockhart (Egea1) and R. Matran (now in Lille); Paris Necker: E. Paty (Egea1–2) and P. Scheinmann (Egea1–2); Paris-Trousseau: A. Grimfeld (Egea1–2) and J. Just. Data and quality management: Inserm ex-U155 (Egea1): J. Hochez; Inserm CESP/U 1018, Villejuif: N. Le Moual; Inserm ex-U780: C. Ravault (Egea1–2); Inserm ex- U794: N. Chateigner (Egea1–2); Grenoble: J. Quentin-Ferran (Egea1–2).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".