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Record W4408760517 · doi:10.1093/humrep/deaf045

Bias due to non-consent in assisted reproductive treatment cohort studies: consent for disclosure to non-contact research in the Human Fertilisation and Embryology Authority register

2025· article· en· W4408760517 on OpenAlexaff
Fiskani J M Kondowe, Matthew Gittins, Peter Clayton, Daniel R. Brison, Stephen A. Roberts

Bibliographic record

VenueHuman Reproduction · 2025
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsInformed consentPopulationCohortFamily medicineCohort studyMedicineParental consentReproductive medicinePsychologyPregnancyAlternative medicineBiologyEnvironmental healthPathologyGenetics

Abstract

fetched live from OpenAlex

STUDY QUESTION: Is patient consent to research associated with the distribution of population characteristics and study outcomes in ART cohort studies? SUMMARY ANSWER: The distribution of population characteristics in the patient consent subset differs from that in the non-consent subset and is not fully representative of the general ART population; thus, study results of population subsets requiring patient consent may be subject to bias. WHAT IS KNOWN ALREADY: Non-consent in epidemiological studies may bias study results if the consent subset differs systematically from the non-consent subset and is thus not representative of the full study population. ART cohort datasets may be biased if they require patients to consent to use their data. As an example, from September 2009 onwards, ART patients in the UK have been asked for specific 'consent to disclosure of identifying information' (CD) for research studies. STUDY DESIGN, SIZE, DURATION: This cohort study utilized an anonymized version of the Human Fertilisation and Embryology Authority (HFEA) dataset containing all CD and non-CD autologous ART treatment cycles (n = 819 512) conducted from 2004 to 2018 in the UK. A live birth (LB) subset of 155 986 singletons born during the same period was used to analyse child outcomes. Additionally, an aggregated version of the HFEA dataset was used to explore CD trends by clinic type (National Health Service [NHS], private, or both NHS and privately funded). PARTICIPANTS/MATERIALS, SETTING, METHODS: The dataset containing all gamete cycles was used to explore factors associated with giving CD and to compare LB outcome trends (number of LBs per yearly treatment cycles started) between CD and non-CD cycles. The LB subset was used to compare the birthweight outcomes (low birthweight (LBW = birthweight < 2500 g or otherwise) and macrosomia (birthweight ≥4000 g or otherwise)) between CD and non-CD cycles. Logistic regression models explored the association between CD and population characteristics and the impact of CD on birthweight outcomes over the calendar years. Each regression model was adjusted for potential confounders: for all models (maternal age, ethnicity, previous IVF cycles, previous pregnancies, previous LBs, causes of infertility (tubal, endometriosis, male factor, ovulatory, unknown), and embryo transfer type and stage); for LB and birthweight models (ICSI, elective single embryo transfer, and ovarian stimulation); and additionally for birthweight models (child sex and gestation). MAIN RESULTS AND THE ROLE OF CHANCE: During the study period, CD rates increased from 16% at its inception in 2009 to 64% in 2018. Fewer cycles from older patients (40-44 years old) and ethnic minorities (Black and Asian) gave CD. Cycles with previous ART treatments and LBs had lower rates of giving CD. CD was also associated with LB rates (higher in the CD group) and LBW (slightly more prevalent in the non-CD group). CD rates were consistently higher in NHS-only funded clinics than in clinics with partly or fully private funding. It may be possible to adjust for much of the post-2009 bias by weighting by the probability of inclusion derived from supplementary data. LIMITATIONS, REASONS FOR CAUTION: Important factors not provided or unavailable in the dataset included socio-economic and lifestyle factors. Additionally, the anonymized dataset provided to us had banded/categorized maternal age, gestation, and birthweight variables, possibly limiting our estimates' precision. WIDER IMPLICATIONS OF THE FINDINGS: This study shows that using only consented data in ART observational cohort studies may result in a sample that differs from the non-consented sample and general ART population. Specifically, our results show differences in the distribution of population characteristics, LB, and LBW outcomes between CD and non-CD groups in the UK HFEA ART register dataset. Careful attention is therefore required when analysing and interpreting these and similar cohort data; failure to consider the impact of consent will likely produce misleading results. In the HFEA register, this applies to research studies using CD data (including bespoke data requests and linkage studies) after the introduction of CD in October 2009. A potential solution weighting by the probability of consent is briefly introduced. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by the EU H2020 Marie Sklodowska-Curie Innovative Training Networks (ITN) grant Dohartnet (H2020-MSCA-ITN-2018-812660). The authors have no competing interests to declare. TRIAL REGISTRATION NUMBER: N/A.

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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)0.021
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.750
GPT teacher head0.635
Teacher spread0.116 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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