Transdermal estradiol as a novel treatment for Peyronie's disease: A case report
Bibliographic record
Abstract
Introduction: This is a single‐patient case report of a (now) 75-year old married male with long-standing Peyronie’s disease (PD). The patient’s main symptoms were painful erections as well as secondary anxiety and depression. Resolution of these symptoms was achieved with transdermal estradiol treatment. Objectives: To describe a patient with PD, whose persistently painful erections and psychologic distress were eliminated by a novel treatment involving transdermal estradiol. This report also aims to present a novel treatment for patients with paraphilias. Methods: An interview with the patient, chart review, and a literature search were conducted. Informed consent was obtained from the patient and this report was approved by the Research Ethics Committee at the Royal Hospital in Ottawa. Results: Treatment with transdermal estradiol decreased the patient’s sex drive and virtually eliminated his erections with no undesirable side effects. The patient and his wife still enjoy sexual relations without the need for penile erection. Conclusions: This case report presents a novel treatment for PD. It also introduces a novel method to treat men who wish to decrease their sex drive (e.g. men with paraphilic disorders). Replication of this treatment intervention in men with PD and new studies of its use in men with paraphilic disorders are warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".