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Record W4220876930 · doi:10.1080/14647273.2022.2051614

Chromosomal polymorphisms in assisted reproduction: a systematic review and meta-analysis

2022· review· en· W4220876930 on OpenAlexaboutno aff
Madara S. B. Ralapanawe, Hajra Khattak, Himashi R. Hapangama, Gimhani R. Weerakkody, Argyro Papadopoulou, Ioannis Gallos

Bibliographic record

VenueHuman Fertility · 2022
Typereview
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMiscarriageMeta-analysisMedicineCohort studyObstetricsGynecologyLive birthPregnancyRelative riskCINAHLCohortInternal medicineBiologyGeneticsConfidence intervalPsychological intervention

Abstract

fetched live from OpenAlex

This systematic review and meta-analysis investigated the effects of chromosomal polymorphisms in reproductive outcomes following IVF or ICSI. Literature in CENTRAL, CINAHL, EMBASE and MEDLINE were searched from 1974 to March 2020 with no language restrictions. Ten published cohort studies were chosen for analysis. Studies included females, males and couples undergoing assisted reproductive treatments with the presence or absence of chromosomal polymorphisms. Reproductive outcomes were reported and their quality assessed using the Newcastle-Ottawa Quality Assessment Scale. Meta-analysis of five cohort studies (9,659 participants) indicated that female carriers with chromosomal polymorphisms had a higher miscarriage rate compared to non-carriers (risk ratio (RR) 1.54 (95% CI 1.19-1.98), whereas no significant association was found for males (RR 0.96, 95% CI 0.64-1.43) and couples (RR 1.93, 95% CI 0.32-11.83) indicating that this effect appeared to be gender-dependent. There was no association between chromosomal polymorphisms and a higher rate of biochemical, clinical, ongoing pregnancy, and preterm and live birth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.022
Bibliometrics0.0070.010
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.296
GPT teacher head0.426
Teacher spread0.130 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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