(298) PRACTICE PATTERNS FOR THE ASSESSMENT OF PATIENTS’ ORGASM CONCERNS BY MEDICAL SPECIALTY
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".