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Record W4377246186 · doi:10.1093/jsxmed/qdad060.185

(193) Sleep Related Painful Erections: A Survey Based Analysis of Patient Reported Experiences with Diagnosis and Management

2023· article· en· W4377246186 on OpenAlexaff
Robert J. Wong, D. Chung, Faysal A. Yafi, Premal Patel

Bibliographic record

VenueThe Journal of Sexual Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Obstructive sleep apneaPsychological interventionDemographicsSexual functionErectile dysfunctionSexual dysfunctionPsychiatryMedical historyInsomniaClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction Sleep related painful erection disorder (SRPE) is a debilitating condition which is currently not well understood. It is characterized by frequent nighttime awakenings due to painful nocturnal erections. Various theories surrounding the pathophysiology of this condition include increased testosterone levels, obstructive sleep apnea, altered autonomic function and psychosomatic factors, amongst others. To our knowledge, there have been no controlled studies investigating this, with literature consisting mostly of case reports. We sought to survey those affected to determine a unifying cause, treatment, and assess the impact on their quality of life Objective Understand the collective experience and unifying themes in the presentation, diagnosis and treatment of patients with SRPE. Methods A group of 63 men diagnosed with SRPE were administered a 30 item questionnaire consisting of both multiple choice, short and long answers from January 2021 to July 2021. Question items were generated based on existing literature. Survey items were reviewed by two academic Urologists specializing in treatment of sexual dysfunction. Information about demographics, clinical history, social history, symptomatology, and both surgical and medical interventions were captured. Quality of life was captured on a 5 point likert scale. Results Overall, 40 patients diagnosed with SRPE responded to the survey. The median age of those surveyed was 43.5 (range 21-69). Majority of respondents (66%, n=21) had no pertinent past medical history related to disorders of erectile function, while 50% (n=20) of subjects reported definitely or possibly having sleep apnea, and 38% (n=12) reported a mental health disorder. The most common current medication among subjects was baclofen (55%, n=22), followed by clonazepam (10%, n=4). However, baclofen proved to be beneficial in only 36% (n=8) of responders who had trialed it. The interventions which were most commonly reported as helpful by those who attempted were sleep repositioning (50%, n=4) and oxygen device use (43%, n=3). Majority of patients have not required intervention in the emergency department 92.5%, n=37), and only 7.5% (n=3) patients have required penile aspiration. Responders described their quality of life as being severely impacted by SRPE, with 14 choosing a severity of 5 (35%). The median score for this response category was 4. Conclusions SRPE is a poorly described condition which should be distinguished from other erectile pathologies such as priapism. The majority of our patients reported OSA, and previous studies have suggested a sleep-cycle association. Sleep reposition and oxygen use appear to provide the most relief for the majority of our patients, although this was not a reliable treatment option. From a medication perspective, there was no consistent class of medication that provides symptom relief. Majority of patients appear to manage their erections without requiring a visit to ED. An overwhelming majority of our patients identified with a mental health disorder, and similarly the average response was that this had a severe impact on their quality of life. Based on these responses and the impact on quality of life, further studies must be done to better elucidate this condition. Disclosure No

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.317
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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