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Record W4248875109 · doi:10.25270/con.2021.01.00011

An Atlas of Lumps and Bumps, Part 2

2021· article· en· W4248875109 on OpenAlexaff
Alexander K. C. Leung, Benjamin Barankin, Joseph S. Lam, Kin Fon Leong

Bibliographic record

VenueConsultant · 2021
Typearticle
Languageen
FieldMedicine
TopicGenital Health and Disease
Canadian institutionsUniversity of British ColumbiaAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsGenital wartsSex organGlansPenisHuman papilloma virusDermatologyGlans penisMedicineTransmission (telecommunications)Condyloma AcuminatumGynecologyVirologyBiologySyphilisHuman papillomavirusAnatomyCervical cancerInternal medicineCancerHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Genital Warts Genital warts (also known as condylomata acuminata or anogenital warts) are the most common clinical manifestations of genital human papilloma virus (HPV) infections.1,2 It is estimated that 1% of sexually active individuals aged 16 to 35 years have clinically evident genital warts. 2 Men are more commonly affected.1 Approximately 90% of genital warts are caused by HPV 6 and HPV 11. 1,2 HPV strains 1, 2, 3, 4, 16, 18, 40, 42, 43, 44, 54, 70, 72, and 81 account for the rest. 2 In adults, genital HPV infection is predominately transmitted by penetrative intercourse and less commonly by oral sex, skin-toskin transmission, and fomites. [1][2]2][3] In children, HPV infection may result from sexual abuse, vertical transmission, autoinoculation, heteroinoculation, and transmission via fomites.2,4 Genital warts are usually asymptomatic but may at times cause discomfort, itching, burning, bleeding, and pain.[1][2][3] They are most commonly found on the external genitalia.In men, genital warts are usually located on the frenulum, glans penis, An Atlas of Lumps and Bumps, Part 2

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.108
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1080.048

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 head0.017
GPT teacher head0.305
Teacher spread0.287 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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