Bibliographic record
Abstract
INTRODUCTION: There is much popular discussion on strategies to facilitate multiple orgasms in men (ie, 100,000+ hits in Google), yet the topic has not received an objective comprehensive review in the literature. AIM: To review the literature on male multiple orgasms. METHODS: We searched the literature for publications on "male multiple orgasms" and factors influencing male multiple orgasms in Google, PubMed, and PsychINFO. This yielded 15 relevant publications. MAIN OUTCOME MEASURES: A comprehensive overview on the topic of male multiple orgasms and factors that influence the propensity of men to experience multiple orgasms. RESULTS: Few men are multiorgasmic: <10% for those in their 20s, and <7% after the age of 30. The literature suggests 2 types of male multiple orgasms: "sporadic" multiorgasms, with interorgasmic intervals of several minutes, and "condensed" multiorgasms, with bursts of 2-4 orgasms within a few seconds to 2 minutes. Multiple orgasms appear physiologically similar to the single orgasm in mono-orgasmic men. However, in a single case study, a multiorgasmic man did not experience with his first orgasm the prolactin surge that usually occurs with orgasm in mono-orgasmic men. Various factors may facilitate multiple orgasms: (1) practicing to have an orgasm without ejaculation; (2) using psychostimulant drugs; (3) having multiple and/or novel sexual partners; or (4) using sex toys to enhance tactile stimulation. However, confirmatory physiological data on any of these factors are few. In some cases, the ability to experience multiple orgasms may increase after medical procedures that reduce ejaculation (eg, prostatectomy or castration), but what factor(s) influence this phenomenon is poorly investigated. CONCLUSION: Despite popular interest, the topic of male multiple orgasms has received surprisingly little scientific assessment. The role of ejaculation and physiological change during the refractory period in inhibiting multiple orgasms has barely been investigated.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".