MétaCan
Menu
← Back to cohort
Record W2157621059 · doi:10.2310/7750.2011.10001

HIV Dermatology in Canada: Cutaneous Disease Burden and Demographic Characteristics of a Specialized Urban Practice

2011· article· en· W2157621059 on OpenAlexaffabout
Jonathan Shapero, Jasmine Leslie, Gillian C. de Gannes

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineVenereologyDermatologyImmunosuppressionDiseaseOutpatient clinicHuman immunodeficiency virus (HIV)AIDS-Related Opportunistic InfectionsCross-sectional studyViral diseaseSidaInternal medicineImmunologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The introduction of antiretroviral therapies has changed the pattern of cutaneous disease in individuals infected with human immunodeficiency virus (HIV). OBJECTIVE: To assess demographic characteristics, severity of immunosuppression, and frequency of dermatologic disorders in patients presenting to a Canadian specialized HIV dermatology practice. METHODS: A cross-sectional study was performed of 183 consecutive outpatient and inpatient consultations to a single HIV dermatology practice from January 2007 to December 2008. RESULTS: One hundred sixty-three (88%) patients were male, with an average age of 45. Forty-six patients were not on antiretroviral therapy. Verruca was the most common diagnosis, seen in 29 patients, followed by dermatophyte infection, seen in 27. Patients with a low CD4 count (p = .0001) and a high viral load (p = .0043) were more likely to present with an HIV-specific dermatosis. CONCLUSION: Cutaneous infections were the most common diagnoses in this cross section. Classic HIV dermatoses were seen more frequently in those with more advanced disease owing to HIV infection.

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 score0.077
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cutaneous Medicine and Surgery→Same topicNail Diseases and Treatments→French-language works237,207→