PD34-01 EVALUATION OF FERTILITY PRESERVATION PRACTICES AMONG ONCOLOGISTS: REVIEW OF ASCO’S QUALITY ONCOLOGY PRACTICE INITIATIVE STANDARDS FOR CANCER CARE
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Résumé
You have accessJournal of UrologyInfertility: Therapy I (PD34)1 Apr 2019PD34-01 EVALUATION OF FERTILITY PRESERVATION PRACTICES AMONG ONCOLOGISTS: REVIEW OF ASCO’S QUALITY ONCOLOGY PRACTICE INITIATIVE STANDARDS FOR CANCER CARE Premal Patel*, Benjamin Shiff, Taylor Kohn, Ridwan Alam, and Ranjith Ramasamy Premal Patel*Premal Patel* More articles by this author , Benjamin ShiffBenjamin Shiff More articles by this author , Taylor KohnTaylor Kohn More articles by this author , Ridwan AlamRidwan Alam More articles by this author , and Ranjith RamasamyRanjith Ramasamy More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556284.03529.1cAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: The ASCO Quality Oncology Practice Initiative is an oncologist-led practice-based quality assessment program to promote excellence in cancer care. A total of 994 practices submit data on over 32,000 patients. We utilized their dataset to identify the proportion and predictors of discussing fertility risks and fertility preservation prior to initiating cancer therapy among patients of reproductive age. METHODS: Reproductive age was defined as 18-40 and 18-50 for females and males, respectively. We assessed whether fertility risks and fertility preservation options were discussed prior to chemotherapy with patients of reproductive age. We also assessed whether a referral to a specialist was made. Multivariable linear regression was performed to identify predictors of fertility preservation counselling controlling for practice type (academic vs. private), geographic location (region and state) and state legislature mandating insurance coverage for fertility preservation. RESULTS: A total of 136,746 charts were reviewed with a total of 27,052 patients identified as being of reproductive age. Overall, 41.8% of patient of reproductive age had a discussion regarding the risk of infertility associated with chemotherapy while 27.7% of patients had fertility preservation options discussed or were referred to a specialist. On multivariable linear regression being seen at an academic institution was associated with more frequent discussion of fertility risk (48.0% vs 40.4%, p = 0.04) and more frequent discussion of fertility preservation options (34.0% vs 25.5%, p = 0.004) when compared with private practices. States in which laws mandate coverage of fertility preservation were associated with significantly higher rates of discussion (48.6% vs 39.6%, p = 0.0003) and more frequent discussion of fertility preservation (32.9% vs 25.1%, p = 0.0003). There has been no increase in either discussion of risk or fertility preservation from 2015 to 2018. State and region were not significantly associated with differences in discussions. CONCLUSIONS: Despite institution of guidelines mandating fertility preservation, less than half of providers appear to discuss risks and options. Providers in academic institutions and providers in states that mandate fertility preservation appear to discuss options more frequently as compared to providers in private practices and states that lack coverage. Further research is necessary to increase fertility preservation awareness aimed at both patients and providers. Source of Funding: Department of Urology, University of Miami Miami, FL; Winnipeg, Canada; Baltimore, MD; Miami, FL© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e644-e645 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Premal Patel* More articles by this author Benjamin Shiff More articles by this author Taylor Kohn More articles by this author Ridwan Alam More articles by this author Ranjith Ramasamy More articles by this author Expand All Advertisement PDF downloadLoading ...
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 | 0,081 | 0,248 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,010 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».