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Record W2995516918 · doi:10.1186/s43043-019-0011-0

Review of assisted reproduction techniques, laws, and regulations in Muslim countries

2019· article· en· W2995516918 on OpenAlexaff
Chokri Kooli

Bibliographic record

VenueMiddle East Fertility Society Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIslamReproductionMulticulturalismLawPolitical scienceEthical issuesSociologyEngineering ethicsHistoryEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract Background Fourteen Muslim countries were explored for available national laws, regulations, and guidelines concerning assisted reproduction techniques (ART). These documents were studied with total consideration of the ethical and religious principles followed by Islamic religion. This study found different types of legal documents issued by legislatives authorities, ethical committees, or professional bodies. Documents reviewed are directly related to assisted reproduction techniques medical use, access, or research in the field of ART. Main body of the abstract Most of the studied documents showed various degrees of deficiencies concerning legal or ethical protections and considerations. Certain documents that were examined need to be updated or amended in order to follow the continuous medical progress. The research also showed certain difficulty of legislating in countries characterized by multiculturalism and different ethical and religious traits and beliefs. Recently, Muslim legislators have made many efforts. However, the spread of legal documents among the Muslim countries is partial in volume and nature. Short conclusion The comparison of the content with international documents shows us that most of assisted reproductions legal documents in use in the studied countries demonstrate numerous deficiencies in term of structure, nature, and the coverage of controversial subjects.

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)0.001
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 score0.158
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.046
GPT teacher head0.302
Teacher spread0.256 · 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

Citations37
Published2019
Admission routes1
Has abstractyes

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