Patient Characteristics by Type of Hypersexuality Referral: A Quantitative Chart Review of 115 Consecutive Male Cases
Bibliographic record
Abstract
Hypersexuality remains an increasingly common but poorly understood patient complaint. Despite diversity in clinical presentations of patients referred for hypersexuality, the literature has maintained treatment approaches that are assumed to apply to the entire phenomenon. This approach has proven ineffective, despite its application over several decades. The present study used quantitative methods to examine demographic, mental health, and sexological correlates of common clinical subtypes of hypersexuality referrals. Findings support the existence of subtypes, each with distinct clusters of features. Paraphilic hypersexuals reported greater numbers of sexual partners, more substance abuse, initiation to sexual activity at an earlier age, and novelty as a driving force behind their sexual behavior. Avoidant masturbators reported greater levels of anxiety, delayed ejaculation, and use of sex as an avoidance strategy. Chronic adulterers reported premature ejaculation and later onset of puberty. Designated patients were less likely to report substance abuse, employment, or finance problems. Although quantitative, this article nonetheless presents a descriptive study in which the underlying typology emerged from features most salient in routine sexological assessment. Future studies might apply purely empirical statistical techniques, such as cluster analyses, to ascertain to what extent similar typologies emerge when examined prospectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".