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Record W3040958639 · doi:10.1111/andr.12858

Phytoestrogen intake and other dietary risk factors for low motile sperm count and poor sperm morphology

2020· article· en· W3040958639 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAndrology · 2020
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsUniversity of Alberta
FundersEuropean Chemical Industry Council
KeywordsSemen qualityConfoundingMedicineSpermOdds ratioLogistic regressionPhytoestrogensInfertilitySemenBiologyInternal medicinePregnancyAndrology

Abstract

fetched live from OpenAlex

Abstract Background Few potentially modifiable risk factors of male infertility have been identified, and while different diets and food groups have been associated with male infertility, evidence linking dietary factors including phytoestrogens and semen quality is limited and contradictory. Objectives To study the associations between phytoestrogen intake and other dietary factors and semen quality. Materials and Methods A case‐referent study was undertaken of the male partners, of couples attempting conception with unprotected intercourse for 12 months or more without success, recruited from 14 UK assisted reproduction clinics. A total of 1907 participants completed occupational, lifestyle and dietary questionnaires before semen quality (concentration, motility and morphology) were assessed. Food intake was estimated by a 65‐item food frequency questionnaire (FFQ) covering the 12 months prior to recruitment. Analyses of dietary risk factors for low motile sperm concentration (MSC: <4.8 × 10 6 /mL) and poor sperm morphology (PM: <4% normal morphology) used unconditional logistic regression, accounting for clustering of subjects within the clinics, first without, and then with, adjustment for confounders associated with that outcome. Results High consumption of daidzein (≥13.74 μg/d), a phytoestrogen found in soy products, was a protective factor for MSC with an odds ratio (95%CI) of 0.58 (0.42‐0.82) after adjustment for clustering and potential confounding. Dietary risk factors for PM after similar adjustment showed that drinking whole milk (OR 0.67, 95%CI 0.47‐0.96) and eating red meat were protective with an OR 0.67 (0.46‐0.99) for eating red meat >3 times/wk. Discussion In this case‐referent study of men attending an infertility clinic for fertility diagnosis, we have identified that low MSC is inversely associated with daidzein intake. In contrast, daidzein intake was not associated with PM but eating red milk and drinking whole milk were protective. Conclusions Dietary factors associated with semen quality were identified, suggesting that male fertility might be improved by dietary changes.

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.

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)0.000
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 score0.121
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.034
GPT teacher head0.296
Teacher spread0.263 · 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