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Enregistrement W2546536696 · doi:10.1093/humrep/dew272

Preconception use of pain-relievers and time-to-pregnancy: a prospective cohort study

2016· article· en· W2546536696 sur OpenAlexaboutno aff
Kathryn A. McInerney, Elizabeth E. Hatch, Amelia K. Wesselink, Kenneth J. Rothman, Ellen M. Mikkelsen, Lauren A. Wise

Notice bibliographique

RevueHuman Reproduction · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueReproductive Health and Contraception
Établissements canadiensnon disponible
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human Development
Mots-clésMedicineProspective cohort studyPregnancyObstetricsCohort studyCohortGynecologyInternal medicineBiology

Résumé

récupéré en direct d'OpenAlex

STUDY QUESTION: To what extent is preconception use of pain-relieving medication associated with female fecundability? SUMMARY ANSWER: Women who used naproxen or opioids had slightly lower fecundability than women who did not use any pain-relieving medications; use of acetaminophen, aspirin and ibuprofen was not appreciably associated with fecundability. WHAT IS KNOWN ALREADY: Over-the-counter pain-relieving medications are commonly used by women of reproductive age in the USA. Studies investigating the effects of pain-relieving medication use on ovulation, implantation and fecundability have shown conflicting results. STUDY DESIGN, SIZE, DURATION: We analyzed data from an internet-based prospective cohort study of 2573 female pregnancy planners aged 21-45 years from the USA and Canada. Participants were enrolled and followed from June 2013 through September 2015. Participants completed a baseline questionnaire and bimonthly follow-up questionnaires until a reported pregnancy or for 12 months, whichever occurred first. Over 80% of participants completed at least one follow-up questionnaire. PARTICIPANTS/MATERIALS, SETTING, METHODS: Use of pain-relieving medication during the past month was assessed at baseline and on each follow-up questionnaire. Medications were categorized according to type (acetaminophen, aspirin, ibuprofen, naproxen and opioids) and total monthly dose. Self-reported pregnancy was assessed at each follow-up. Multivariable-adjusted fecundability ratios (FRs) and 95% CI were calculated using proportional probabilities regression. Models were adjusted for demographic, lifestyle and anthropometric factors; reproductive history; gynecologic morbidity; and indications for use of pain medications. Models were also run with and without adjustment for parity. After restricting to women with 6 or fewer months of attempt time at study entry, 1763 were included in the analyses. MAIN RESULTS AND THE ROLE OF CHANCE: At baseline, 1279 (73%) women reported using ≥1 pain-relieving medications in the previous month. When compared with non-use of pain-relieving medications, FRs for use of naproxen and opioids at baseline were 0.78 (95% CI: 0.64-0.97) and 0.81 (95% CI: 0.60-1.10), respectively. A dose-response relation was observed between naproxen use and fecundability; FRs for use of <1500 and ≥1500 mg of naproxen were 0.85 (95% CI: 0.68-1.07) and 0.58 (95% CI: 0.36-0.94), respectively. Small numbers (n = 74) precluded the examination of opioid use by dose. Overall, there was little evidence of an association between fecundability and acetaminophen (FR 1.04, 95% CI: 0.92-1.18), aspirin (FR 1.00, 95% CI: 0.80-1.25), or ibuprofen (FR 1.00, 95% CI: 0.89-1.11). Similar results were observed when exposure information was updated over time. LIMITATIONS, REASONS FOR CAUTION: Numbers of opioid users were small. Information collected on reason for use of pain medications was not specific to each type of pain medication. Therefore, we cannot rule out confounding by indication as an explanation of these results. WIDER IMPLICATIONS OF THE FINDINGS: Use of naproxen and opioids was associated with a small reduction in fecundability, but there was little association between other pain-relieving medications and fecundability. STUDY FUNDING/COMPETING INTERESTS: This study was supported through funds provided by National Institute of Child Health and Human Development, National Institute of Health (R21 HD072326, T32 HD052458). The authors have no conflicts of interest to disclose. TRIAL REGISTRATION NUMBER: Not applicable.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,220
Score d'incertitude au seuil0,423

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,030
Tête enseignante GPT0,295
Écart entre enseignants0,266 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations24
Publié2016
Routes d'admission1
Résumé présentoui

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