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Record W2141207751 · doi:10.1111/jsm.12979

Standardization of Penile Plethysmography Testing in Assessment of Problematic Sexual Interests

2015· review· en· W2141207751 on OpenAlexaffabout
Lisa Murphy, Rebekah Ranger, J. Paul Fedoroff, Hannah J. Stewart, Ross G. Dwyer, William H. Burke

Bibliographic record

VenueThe Journal of Sexual Medicine · 2015
Typereview
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsResearch CanadaUniversity of OttawaRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsStandardizationSexual arousalArousalStimulus (psychology)PsychologyMedicineClinical psychologyCognitive psychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Penile plethysmography (PPG) is an objective measure of sexual arousal for men, commonly used to assess sexual arousal to both abnormal (i.e., paraphilic) and normal stimuli. While PPG has become a standard measure in the assessment and treatment of male sex offenders and men with paraphilic interests in both Canada and the United States, there is a lack of standardization of stimulus sets and interpretation of results between sites. The current article critically reviews the current state of the art while highlighting clinical and research efforts that may be undertaken in an attempt to reduce issues arising from lack of standardization across sites. Types and themes of stimulus sets, assessment apparatuses, laboratory preparation, and testing procedures are discussed. The continued development of standardized testing protocol and procedures across multiple international sites continues to be encouraged to promote unified PPG administration and interpretation, thus further enhancing the practical utility of the measurements and decreasing inter-rater discrepancies and error.

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)0.007
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.199
GPT teacher head0.485
Teacher spread0.286 · 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
GenreReview

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

Citations29
Published2015
Admission routes2
Has abstractyes

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