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Record W2021718821 · doi:10.3402/ijch.v63i1.17647

The prevalence of selected pregnancy outcome risk factors in the life-style and medical history of the delivering population in north-western Russia

2004· article· en· W2021718821 on OpenAlexaff
Arild Vaktskjold, Erna Elise Paulsen, Ljudmila Talykova, Evert Nieboer

Bibliographic record

VenueCircumpolar health supplements · 2004
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePregnancyPopulationAbortionDemographyObesityEnvironmental healthEpidemiologyObstetrics

Abstract

fetched live from OpenAlex

OBJECTIVES: A population-based birth registry has been set up for the Arctic town of Moncegorsk in north-western Russia. This investigation describes the health status of the delivering population, including pregnancy history and the prevalence of obesity, infections, smoking and alcohol abuse during the pregnancy period. An overview of the occupations of the delivering population is also presented. METHODS: The birth registry contains detailed and verified information about the newborn, delivery, pregnancy and the mother for 21,214 births by women from Moncegorsk in the period 1973-97. RESULTS: Of the delivering women, 15.7% had experienced one or more spontaneous abortions, and 47.4% had at least one induced abortion. More than 9% had suffered pelvic inflammatory disease (PID) in their past. The local nickel company employed 9016 (42.5%) of the delivering women; of these 17% worked in production areas with exposures to compounds of nickel, among other hazards, and 38% are judged to have had possible, or probable, exposure of this type. CONCLUSION: Compared with the delivering population in Norway, that in Moncegorsk was younger and had a lower prevalence of obesity, diabetes and heavy smoking. The most worrisome findings were the high prevalence of a history of abortion and PID. A relatively high proportion of the women worked in physically demanding, or/and nickel-exposed occupations.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.029
GPT teacher head0.316
Teacher spread0.287 · 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

Citations20
Published2004
Admission routes1
Has abstractyes

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