098 Persistent Genital Arousal Disorder: A Public Health Concern with Low Awareness
Bibliographic record
Abstract
Persistent genital arousal disorder (PGAD) is spontaneous ongoing genital arousal not associated with sexual feelings or activity. It is a serious health concern that can substantially impact patients. To explore public awareness of this issue through internet search trends and understand the quality of the information available to the public. We queried Google Trends to evaluate relative search volume (RSV) from October 2013 to October 2018 of “persistent genital arousal disorder”, “PGAD, and “persistent genital arousal syndrome”. To assess RSV compared to another topic in sexual dysfunction the terms were compared to “erectile dysfunction” (ED). The quality of information available on the internet was assessed using Health On the Net Foundation (HON) certification. Each term was searched in Google and the first two pages of searches were assessed for HON certification. RSV in the United States and internationally on google trends did not show an increase in interest over time. The search term “PGAD” had a higher RSV than “persistent genital arousal disorder” or “persistent genital arousal syndrome” (figure 1). When comparing these terms to the RSV of ED, ED was consistently a more popular search term with an RSV ranging from 30-100 compared to the PGAD terms with RSVs ranging from 0-4. States with the highest RSV for PGAD were Massachusetts (100), Pennsylvania (95), Minnesota (91), Kentucky (91), and Wisconsin (89). Countries with the highest RSV for PGAD were South Africa (100), United States (99), South Korea (87), Canada (85), and Sweden (83). Quality assessment showed 28% of persistent genital arousal disorder, 16% OF PGAD, 33% of persistent genital arousal syndrome, and 63% of erectile dysfunction search Results were HON certified.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".