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Record W339403336 · doi:10.1177/120347540000400202

Sex Differences in the Anatomical Distribution of Melanocytic Nevi in Canadian Hutterite Children

2000· article· en· W339403336 on OpenAlexaffabout
Tammi Y. Kwan, Terry W. Belke, Tom Enta

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2000
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsUniversity of CalgaryMount Allison UniversityDalhousie University
Fundersnot available
KeywordsMedicineNevusMelanomaPhysiologySun exposureMelanocytic nevusDemographyDistribution (mathematics)Dermatology

Abstract

fetched live from OpenAlex

BACKGROUND: Studies of the distribution of melanocytic nevi (MN) and/or cutaneous malignant melanoma (CMM) in white populations have commonly observed greater numbers of MN and CMM on the torsos of males and on the limbs of females. The most commonly cited explanation for this sex difference is differential sunlight exposure of body subsites due to gender differences in clothing styles and recreational activities. Less common, but more speculative, explanations suggest hormonal differences or regional differences in melanocytes across body subsites. OBJECTIVE: The purpose of the present study was to evaluate these explanations in light of sex differences in the anatomical distribution of nevi in Canadian Hutterite children whose traditional religious costume protects them from sun exposure. METHODS: Nevi counts from 178 male and 154 female children, aged 5 to 15 years, from 23 Central Alberta Hutterite colonies were broken down by age and body subsite. RESULTS: At age levels from 6 to 15 years, males had greater nevus counts on the torso, whereas females had greater counts on the upper and lower limbs. CONCLUSION: The appearance of this distribution of nevi in sun-protected children as early as age 6 is problematic for explanations based on differential sunlight exposure and hormonal changes at puberty.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.090
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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

Citations19
Published2000
Admission routes2
Has abstractyes

Explore more

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