(300) SEXUAL HEALTH PRACTITIONER PATTERNS FOR THE ASSESSMENT OF PATIENTS’ ORGASM CONCERNS
Bibliographic record
Abstract
Abstract Introduction Medical knowledge of orgasm dysfunction has shifted in recent years to include a wide range of conditions that impact multiple domains of orgasm. It is currently unknown how sexual medicine practitioners are adjusting their practice patterns to ensure comprehensive assessment of patients’ orgasm concerns. Objective This study aims to elucidate the current state of orgasm assessment by surveying sexual medicine practitioners. This study’s primary outcome was to identify the method of orgasm assessment utilized by sexual medicine practitioners, exploring both the use of patient reported outcome measures (PROMs) and patient interviews. A secondary outcome was to explore practitioner reasons for utilizing their assessment tool as well as satisfaction with their current practice pattern. 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 and free response. Qualtrics software was used to create, distribute, and analyze the survey and NVivo14 was used to perform qualitative analysis. Results Of the 87 survey responses analyzed, 86 (99%) practitioners felt that it is important to assess orgasm as part of sexual health. Most respondents utilized interviews, with 56% of respondents using patient interviews alone and 34% combining interviews with validated PROMs. Regardless of the assessment modality, approximately 25% of the respondents expressed dissatisfaction with the current modalities for assessing orgasm function. Qualitative responses emphasized the benefits of interviews, including their ability to support detailed and individualized care. Additional barriers to using PROMs included practice limitations such as lack of time and practitioner lack of knowledge about and confidence in orgasm-specific PROMs. Conclusions This study’s findings suggest a significant reliance on interviews over standardized PROMs for assessing orgasm, likely due to the complex, multifactorial nature of orgasm dysfunction as well as lack of knowledge about or confidence in existing orgasm-specific PROMs. Respondents’ dissatisfaction with existing assessment modalities indicates a potential gap in adequate tools to address orgasm dysfunction. Future research should focus on developing more comprehensive, patient-centered assessment strategies that retain qualities of interviews and PROMs. Disclosure No.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".