MP21-06 ONE SIZE DOES NOT FIT ALL: VARIATIONS BY ETHNICITY IN DEMOGRAPHIC CHARACTERISTICS OF MEN SEEKING FERTILITY TREATMENT ACROSS NORTH AMERICA
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You have accessJournal of UrologyInfertility: Epidemiology & Evaluation I (MP21)1 Sep 2021MP21-06 ONE SIZE DOES NOT FIT ALL: VARIATIONS BY ETHNICITY IN DEMOGRAPHIC CHARACTERISTICS OF MEN SEEKING FERTILITY TREATMENT ACROSS NORTH AMERICA Andrew Chen, Keith Jarvi, Katherine Lajkosz, James Smith, Kirk Lo, Ethan Grober, Jared Bieniek, Robert Brannigan, Victor Chow, Trustin Domes, James Dupree, Marc Goldstein, Jason Hedges, James Hotaling, Edmund Ko, Peter Kolettis, Ajay Nangia, Jay Sandlow, David Shin, Aaron Spitz, J Trussell, Scott Zeitlin, Armand Zini, and Mary Samplaski Andrew ChenAndrew Chen More articles by this author , Keith JarviKeith Jarvi More articles by this author , Katherine LajkoszKatherine Lajkosz More articles by this author , James SmithJames Smith More articles by this author , Kirk LoKirk Lo More articles by this author , Ethan GroberEthan Grober More articles by this author , Jared BieniekJared Bieniek More articles by this author , Robert BranniganRobert Brannigan More articles by this author , Victor ChowVictor Chow More articles by this author , Trustin DomesTrustin Domes More articles by this author , James DupreeJames Dupree More articles by this author , Marc GoldsteinMarc Goldstein More articles by this author , Jason HedgesJason Hedges More articles by this author , James HotalingJames Hotaling More articles by this author , Edmund KoEdmund Ko More articles by this author , Peter KolettisPeter Kolettis More articles by this author , Ajay NangiaAjay Nangia More articles by this author , Jay SandlowJay Sandlow More articles by this author , David ShinDavid Shin More articles by this author , Aaron SpitzAaron Spitz More articles by this author , J TrussellJ Trussell More articles by this author , Scott ZeitlinScott Zeitlin More articles by this author , Armand ZiniArmand Zini More articles by this author , and Mary SamplaskiMary Samplaski More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002006.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: While over 50% of infertility cases have a male component, there is scant data on what factors are associated with males seeking fertility evaluation. We aim to evaluate the impact of race and ethnicity on male reproductive history and care. METHODS: Anonymous patient surveys were collected at 22 North American fertility centers from 01/2014-01/2020. Domains included demographics, fertility history, and fertility-related medical, social, and procedure history. Patients were grouped by race, with differences in categorical and continuous outcomes assessed using Fisher’s Exact test and Mann-Whitney U test. RESULTS: A total of 6462 men were surveyed, of which 3320 (51%) were White, 1302 (20%) Asian/Indo-Canadian/Indo-American, 392 (6%) Black, 67 (1%) Indian/Native, 8 (0%) Native Hawaiian/Other Pacific Islander, 1373 (21%) identified as “other”, and 122 (1.9%) did not respond. White males were more likely to seek male factor evaluation sooner (3.5 vs 3.8 years, p<0.001), have older partners (33.3 vs 32.9 years, p=0.008), use exogenous testosterone (1.3% vs 0.6%, p<0.003), use steroids (1.3% vs 0.6%, p=0.007) and to have had a vasectomy (8.4% vs 2.9%, p<0.001) as compared to other races. Black males were more likely to be older than other races (38.0 vs 36.5 years, p<0.001), seek male factor evaluation later (4.8 vs 3.6±4.4 years, p<0.001), less likely to have had a vasectomy (3.3% vs 5.9%, p=0.033), less likely to have partners that underwent intrauterine insemination (IUI) (8.2% vs 12.6%, p=0.009). Asian/Indo-Canadian/Indo-American patients were more likely to be younger (36.1 vs 36.7 years, p=0.012), with younger partners (32.8 vs 33.2 years, p=0.021), less likely to have had a vasectomy (1.2% vs 6.9%, p<0.001), and more likely to have had partners that underwent IUI or in vitro fertilization (IVF) (14.2% vs 11.9%, p=0.024 and 8.0% vs 6.3%, p=0.021, respectively). Native/Indians were more likely to wait longer before pursuing evaluation (5.1 vs 3.6 years, p=0.035) and more likely to have had a vasectomy (13.4% vs 5.7%, p=0.014). CONCLUSIONS: This is the first data looking at racial differences for males undergoing male fertility evaluation by a reproductive urologist. Racial differences exist, and a better recognition and understanding of these, in conjunction with societal and biologic factors can guide personalized care. This is an opportunity to better understand and address disparities in access to fertility care. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e348-e348 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Andrew Chen More articles by this author Keith Jarvi More articles by this author Katherine Lajkosz More articles by this author James Smith More articles by this author Kirk Lo More articles by this author Ethan Grober More articles by this author Jared Bieniek More articles by this author Robert Brannigan More articles by this author Victor Chow More articles by this author Trustin Domes More articles by this author James Dupree More articles by this author Marc Goldstein More articles by this author Jason Hedges More articles by this author James Hotaling More articles by this author Edmund Ko More articles by this author Peter Kolettis More articles by this author Ajay Nangia More articles by this author Jay Sandlow More articles by this author David Shin More articles by this author Aaron Spitz More articles by this author J Trussell More articles by this author Scott Zeitlin More articles by this author Armand Zini More articles by this author Mary Samplaski More articles by this author Expand All Advertisement Loading ...
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,024 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».