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Record W2212927909

Penile length of normal boys in Taiwan.

2007· article· en· W2212927909 on OpenAlexaff
Chung Hsing Wang, Da Tian Bau, Chang Hai Tsai, Da Cheng Liu, Fuu Jen Tsai

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSexual Differentiation and Disorders
Canadian institutionsTerry Fox Research Institute
Fundersnot available
KeywordsMedicineMicropenisPenisSex organPediatricsReference valuesSurgeryHypospadiasInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Micropenis is usually defined as a penis that has a stretched length less than 2.5 standard deviations below the mean value. The purpose of this study was to establish a database and set referable standards of penile length for Taiwanese boys. METHODS: A total number of 2126 boys (156 male newborns, 1198 male infants under 2 years old, and 772 boys older than 2 years old) were included in this study. We excluded those boys with congenital anomaly, frankly genital anomaly and congenital heart disease. Both stretched and flaccid penile lengths were measured for comparison. RESULTS: Our data revealed that the average penile length increased with chronologic age (about 3 cm in neonates, 4 cm when 1 year old, and near 5 cm when 5 years old). But it also revealed that Taiwanese boys have slightly shorter stretched penile length after newborn period till 5 years old when compared with Caucasian boys (all P values < 0.05). CONCLUSIONS: Normal stretched penile length varied between different ethic groups, and maybe body size contribute more or less to smaller penile size in Taiwanese boys compared to Caucasian boys in light of general knowledge that Caucasian is taller than Chinese (including Taiwanese) in average.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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 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

Citations19
Published2007
Admission routes1
Has abstractyes

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