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Record W2128676366 · doi:10.1183/09031936.06.00093005

Do respiratory symptoms predict job choices in teenagers?

2006· article· en· W2128676366 on OpenAlexaff
Katja Radon, S. Huemmer, Holger Dressel, Doris Windstetter, Gudrun Weinmayr, S. K. Weiland, Elena Riu, Christian Vogelberg, W Leupold, Erika von Mutius, Mark S. Goldberg, Dennis Nowak

Bibliographic record

VenueEuropean Respiratory Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsMcGill University
FundersEuropean Respiratory Society
KeywordsMedicineRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

Existing guidelines advise adolescents with asthma and allergies against high-risk occupations. The aim of the current authors' analyses was to investigate the resulting self-selection in a prospective cohort study. The participants of Phase II of the International Study of Asthma and Allergies in Childhood in Germany (aged 9-11 yrs at baseline) were re-contacted after 7 yrs (response rate was 77%) and were asked to complete a questionnaire, which included items on atopic diseases. The subjects were also asked about the type of job they would like to have in the future (preferred job choice). Exposure to agents with potential asthma risk was evaluated using a job exposure matrix. The analyses were restricted to those in school-based vocational training programmes without occupational exposures. A total of 33% of subjects chose jobs with high asthma risk, 23% selected low asthma risk jobs and the remaining adolescents indicated jobs without known asthma risk (reference category). There were no statistically significant associations between asthma, allergic rhinitis or atopic dermatitis and selecting jobs with asthma risk. Participants with allergic rhinitis tended to select high risk jobs less frequently. In conclusion, self-selection into low risk jobs seems to play a minor role in teenagers with asthma or allergies.

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 score0.293
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.271
Teacher spread0.253 · 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

Citations54
Published2006
Admission routes1
Has abstractyes

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