(298) PRACTICE PATTERNS FOR THE ASSESSMENT OF PATIENTS’ ORGASM CONCERNS BY MEDICAL SPECIALTY
Notice bibliographique
Résumé
Abstract Introduction Physicians and advanced practice providers are increasingly seeing patients with orgasm dysfunction conditions that impact its multiple domains. Due to the multifactorial nature of orgasm dysfunction, multiple medical specialties are often consulted for conditions such as post-orgasmic illness syndrome (POIS) and persistent genital arousal disorder/genitopelvic dysesthesia (PGAD/GPD). However, little is known about how different medical specialties identify and treat these conditions. Objective This study aims to identify trends in orgasm assessment by medical doctors (MDs) osteopathic doctors (DOs), physician associates (PAs) and nurse practitioners (NPs) in different medical specialties. In particular, we seek to identify and compare the method of orgasm assessment utilized by physicians and advanced practice providers in various medical specialties and to explore provider satisfaction with their practice pattern by medical specialty. Methods Data were collected via an anonymous 33-question survey distributed from January 7, 2024 to April 14, 2024. The survey asked participants about their practice patterns for assessing orgasm via multiple choice. Qualtrics software was used to create, distribute, and analyze the survey, and Fisher’s exact tests were performed at the p < 0.05 significance level. Results Of the 87 survey responses analyzed, 50 (57%) were physicians or advanced practice providers, with 36 (41%) MDs or DOs, 3 (3%) PAs and 11 (13%) NPs. The majority of these respondents worked in obstetrics and gynecology (Ob/Gyn) (20, 40%) followed by urology/urogynecology (11, 22%), and sexual medicine (8, 16%). The rest of the respondents worked in primary care/family medicine, internal medicine, psychiatry, and infectious disease. Sexual medicine specialists were significantly more likely to use interviews when compared to the other top two specialties (87.5% vs. 44.8%, p < 0.05). Urologists/urogynecologists, with practice patterns split between Patient Reported Outcome Measures (PROMs) plus interviews (45.5%) and interviews alone (54.5%), were significantly more likely to disagree that they are satisfied with the care they provide pertaining to orgasm than all other specialties (45.5% vs 12.5%, p < 0.05). Conclusions This study’s findings identify differences in practice patterns for assessing patients’ orgasm concerns between medical specialties, with sexual medicine specialists relying heavily on interviews over PROMs and providers within other specialties more frequently including PROMs in addition to interviews. Urogynecologists and urologists were the most evenly split with respect to whether their practice patterns including PROMs, and they were also the least likely to be satisfied with their orgasm care. Further studies should investigate reasons for provider dissatisfaction with their orgasm practice patterns and should look into creating standardized guidelines for the workup of orgasm dysfunction across specialties. Disclosure No.
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 | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».