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Record W46122985 · doi:10.1503/jpn.150003

Deep brain stimulation to reduce sexual drive

2015· article· en· W46122985 on OpenAlexvenueno aff
Johannes Fuß, Matthias K. Auer, Sarah V. Biedermann, Peer Briken, Werner Hacke

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2015
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDeep brain stimulationSexual dysfunctionTestosterone (patch)Sexual stimulationPsychological interventionMedicinePsychologyNeurosciencePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

To date there are few treatment options to reduce high sexual drive or sexual urges in paraphilic patients with a risk for sexual offending. Pharmacological therapy aims to reduce sexual drive by lowering testosterone at the cost of severe side effects. We hypothesize that high sexual drive could also be reduced with deep brain stimulation (DBS) of circuits that generate sexual drive. This approach would help to avoid systemic side effects of antiandrogenic drug therapies. So far the best investigated target to reduce sexual drive is the ventromedial hypothalamus, which was lesioned unilaterally and bilaterally by stereotaxic interventions in paraphilic patients in the 1970s. Here, we discuss DBS as a treatment strategy in patients with severe paraphilic disorders with a serious risk of sexual offending. There are profound ethical and practical issues associated with DBS treatment of paraphilic patients that must be solved before considering such a treatment approach.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.334
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations27
Published2015
Admission routes1
Has abstractyes

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