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Record W3156995317 · doi:10.1111/ijcp.13887

Association of human papillomavirus and systemic sclerosis: A population based cohort study

2021· article· en· W3156995317 on OpenAlexaboutno aff
Ming‐Li Chen, Jing‐Yang Huang, Yao‐Min Hung, James Cheng‐Chung Wei

Bibliographic record

VenueInternational Journal of Clinical Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProportional hazards modelHazard ratioInternal medicineEpidemiologyIncidence (geometry)Cumulative incidenceCohortPopulationOncologyConfidence interval

Abstract

fetched live from OpenAlex

Association of human papillomavirus and systemic sclerosis: A population based cohort studySystemic sclerosis (SSc) is an uncommon rheumatic disease characterised by fibrosis of skin and vasculopathy.1 Human papillomavirus (HPV) is known as an independent risk factor for multiple autoimmune disorders and malignancies.2,3 Previous study revealed that co-infections of multiple HPV subtypes were near two times more frequent in the SSc group.4 A significant positive association between self-reported abnormal Pap test and diffuse skin involvement in SSc patients were addressed in a Canadian study.5 However, no previous studies on the epidemiological relationship between HPV infection and SSc has been investigated.To assess the relationship between HPV infection and SSc, we analysed the information present in the 1997-2013 Taiwan's National Health Insurance Research Database.The baseline characteristics among groups are presented in Table 1.Demographic data between the HPV study group and comparison group were analysed by the chi-squared (χ2) tests.Cox proportional hazard regression model

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)0.001
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.438
Teacher spread0.361 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Clinical PracticeSame topicSystemic Sclerosis and Related DiseasesFrench-language works237,207