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Record W2345331895 · doi:10.1177/1203475416649138

Experiences From a Combined Dermatology and Rheumatology Clinic

2016· article· en· W2345331895 on OpenAlexaff
Michael Samycia, Collette McCourt, Kam Shojania, Sheila Au

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsResearch CanadaSt. Paul's HospitalProvidence Health CareUniversity of British Columbia
Fundersnot available
KeywordsMedicineRheumatologyDermatologyMedical diagnosisPsoriatic arthritisPsoriasisRheumatoid arthritisRetrospective cohort studyInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: The Dermatology and Rheumatology Treatment Clinic is a novel multidisciplinary clinic where patients are concomitantly assessed by a rheumatologist and dermatologist. OBJECTIVES: To determine the number of patients seen in clinic, patient demographics, and most common diagnoses. METHOD: A retrospective review was performed over a 2-year period. Data collected included patient age, sex, dermatologic diagnosis, rheumatologic diagnosis, biopsies performed, and number of follow-up visits. RESULTS: A total of 320 patients were seen (78% female, 22% male). The most common rheumatologic diagnoses were systemic lupus erythematosus (18%), rheumatoid arthritis (15%), psoriatic arthritis (13%), and undifferentiated connective tissue disease (8%). The most common dermatologic diagnoses were dermatitis (17%), psoriasis (11%), cutaneous lupus (7%), various types of alopecia (6%), and infections (5%). CONCLUSIONS: Skin diagnoses were often unrelated to the underlying rheumatologic diagnosis. Rheumatologists and dermatologists can both benefit from being aware of the dermatologic conditions that rheumatologic patients are experiencing.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.026
GPT teacher head0.294
Teacher spread0.268 · 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 designCase report
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

Citations20
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of Cutaneous Medicine and SurgerySame topicRheumatoid Arthritis Research and TherapiesFrench-language works237,207