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Record W2053909938 · doi:10.1097/jom.0b013e3181b9ae2f

Reproductive History and Adverse Pregnancy Outcomes in Commercial Flight Crew and Air Traffic Control Officers in the United Kingdom

2009· article· en· W2053909938 on OpenAlexaff
Isabel dos‐Santos‐Silva, Costanza Pizzi, Anthony Evans, Sally Evans, Bianca De Stavola

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicMedical and Agricultural Research Studies
Canadian institutionsInternational Civil Aviation Organization
FundersCancer Research UK
KeywordsCrewOdds ratioMiscarriageMedicinePregnancyCongenital malformationsObstetricsDemographyAeronauticsEngineeringInternal medicineBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine reproductive outcomes among flight crew. METHODS: Ten thousand two hundred five flight crew members and a control group of 2118 air traffic control officers completed a questionnaire in 2001 to 2004, United Kingdom. RESULTS: Similar proportions of flight crew and air traffic control officers reported having ever had difficulties in conceiving a baby. Risks of miscarriages and congenital malformations among pregnancies fathered by men who did not differ by occupation, but stillbirth risk was higher among flight crew (odds ratio = 2.85; 95% CI = 1.30-6.23). Among pregnancies reported by women, risks of miscarriage and stillbirth did not differ by occupation but risk of congenital malformations was higher among flight crew (odds ratio = 2.37; 95% CI = 0.43-13.06). CONCLUSIONS: Flight crew-related exposures were not associated with adverse reproductive outcomes except for possible links, based on small numbers, between paternal exposure and stillbirths and maternal exposure and congenital malformations.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.328
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2009
Admission routes1
Has abstractyes

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