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Record W2740199637 · doi:10.14740/jocmr3105w

Impact of the Great East Japan Earthquake and Fukushima Nuclear Power Plant Accident on Assisted Reproductive Technology in Fukushima Prefecture: The Fukushima Health Management Survey

2017· article· en· W2740199637 on OpenAlexvenueno aff
Masako Hayashi, Keiya Fujimori, Seiji Yasumura, Akihito Nakai

Bibliographic record

VenueJournal of Clinical Medicine Research · 2017
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsnot available
Fundersnot available
KeywordsFukushima Nuclear AccidentMedicineAssisted reproductive technologyNuclear power plantNuclear plantIncidence (geometry)In vitro fertilisationNuclear disasterDemographyEnvironmental healthPregnancyInfertilityEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of the study was to evaluate the incidences and obstetric outcomes of women who conceived using assisted reproductive technology (ART) procedures in Fukushima Prefecture before and after the Great East Japan Earthquake and Fukushima nuclear power plant accident. METHODS: Information was collected and analyzed from 12,070 women who conceived with or without ART in Fukushima Prefecture during the 9 months before and after the disaster. RESULTS: fertilization-embryo transfer (IVF-ET), respectively. The proportion of women who conceived with IVF-ET decreased during the 2 months immediately after the disaster, but returned to pre-disaster levels 3 months after the disaster. In the case of women who conceived without IVF-ET, the incidences of preterm birth and low birth weight increased after the disaster. In contrast, women who conceived with IVF-ET did not differ significantly in obstetric outcomes before and after the disaster but had a higher incidence of cesarean section and low birth weight compared to those conceived without IVF-ET, regardless of the study period. CONCLUSION: The influence of the disaster on woman who conceived using ART procedures was minimal.

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)0.022
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
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 head0.233
GPT teacher head0.519
Teacher spread0.286 · 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.

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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

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