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Record W4396599176 · doi:10.3390/pediatric16020029

Assisted Reproductive Technologies: A New Player in the Foetal Programming of Childhood and Adult Diseases?

2024· article· en· W4396599176 on OpenAlexaff
Gavino Faa, Mirko Manchia, Vassilios Fanos

Bibliographic record

VenuePediatric Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineCertaintyAssisted reproductive technologyMultidisciplinary approachNarrativeNarrative reviewDevelopmental psychologyPsychiatryPsychologyPregnancyIntensive care medicineInfertility

Abstract

fetched live from OpenAlex

Assisted reproductive technology (ART) is an emerging field in medicine that incorporates complex procedures and has profound ethical, moral, social, religious, and economic implications not just for the individuals who have access to this method but also for society. In this narrative review, we summarise multiple aspects of ART procedures and the possible consequences on the mother and newborn. Moreover, we provide an overview of the possible long-term consequences of ART procedures on the health of newborns, although longitudinal evidence is particularly scant. Users should be informed that ART procedures are not risk-free to prepare them for the possible negative outcomes that may occur in the perinatal period or even in childhood and adulthood. Indeed, risk estimates point to increased liability for major nonchromosomal birth defects; cardiovascular, musculoskeletal, and urogenital (in male newborns) defects; and any other birth defects. Less certainty is present for the risk of neuropsychiatric sequelae in children conceived through ART. Thus, its application should be accompanied by adequate counselling and psychological support, possibly integrated into specific multidisciplinary clinical programmes.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.270
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.263
Teacher spread0.254 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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