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Record W7098166872

Mount Sinai Hospital, Toronto

2016· article· en· W7098166872 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry Medicinal Plant Applications
Canadian institutionsnot available
Fundersnot available
KeywordsLuteinizing hormoneTestosterone (patch)Follicle-stimulating hormoneHormoneGonadotropinAndrogen
DOInot available

Abstract

fetched live from OpenAlex

Twenty-six pedophiles and 16 nonviolent nonsex offenders were compared on baseline values of Luteinizing hormone (LH), follicle stimulating hormone (FSH), testosterone, estradiol, dehydroenpiandrosterone sulphate (DHEAS) and cortisol. Pedophiles had significantly higher levels of LH and FSH but lower levels of testosterone. There were no significant differences on the remaining hormones. When age and substance abuse were controlled, LH and FSH differences were not statistically significant but testosterone differences remained and pedophiles now had lower levels of cortisol. In a second study, 26 pedophiles and 14 healthy community controls were compared on the gonadotropin releasing hormone (GnRH) test. Blood was sampled for LH and FSH at times- 15, 0, 20, 45 and 60 minutes. There were no group differences in baseline values of LH or FSH. Pedophiles, however, showed greater increases in LH (but not FSH) than controls after GnRH injection. Results were similar when age, substance abuse and baseline levels of testosterone were taken into account. The findings suggest that further investigation of pituitary functioning in pedophiles is warranted.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.681
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3190.064

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same topicPhytochemistry Medicinal Plant ApplicationsFrench-language works237,207