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Record W2592965471 · doi:10.1515/bjmg-2016-0038

The genetic background of Southern Iranian couples before marriage

2016· article· en· W2592965471 on OpenAlexaff
Ali Nariman, Mohammad Reza Sobhan, M Savaei, Erfan Aref‐Eshghi, R Nourinejad, Mehdi Manoochehri, Shahnaz Ghahremani, F Daliri, K Daliri

Bibliographic record

VenueBalkan Journal of Medical Genetics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsConsanguinityGenetic counselingReferralMedicineMarital statusDemographyFamily medicineFamily historyConsanguineous MarriagePediatricsEnvironmental healthPopulationGeneticsBiologySociologySurgery

Abstract

fetched live from OpenAlex

Abstract Genetic service for couples plays an increasingly important role in diagnosis and risk management. This study investigated the status of consanguinity and the medical genetic history (effectiveness and coverage of medical genetic services) in couples residing in a city in southern Iran. We questioned couples who were referred to Behbahan Marital Counseling Center, Behbahan, Iran, during the period from January to November 2014, to obtain information on consanguinity, disease history, and previous referral to a medical genetics center. For the collected data was obtained descriptive statistics with STATA 11.0 software. A total of 500 couples were questioned. Mean age was 24.8 ± 5.2 years. Almost one quarter (23.4%) of the couples were consanguineous. Consanguinity was almost twice as common in rural areas as in urban areas (33.9 vs . 19.2%, p = 0.001). Only a few couples (~3.0%) had ever been referred for genetic counseling. The main reason for previous genetic counseling was consanguinity (85.7%). The majority of the participants (96.3%) had never been tested for any genetic conditions. Our findings suggest that only a small proportion of couples in Khuzestan Province, Iran (Behbahan City) were receiving adequate genetics care. This may reflect the limited accessibility of such services, and inadequate awareness and education among the care providers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 designNot applicable
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

Citations2
Published2016
Admission routes1
Has abstractyes

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