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Record W3201603123 · doi:10.1016/j.jaad.2021.09.016

Biologic therapy is not associated with increased COVID-19 severity in patients with hidradenitis suppurativa: Initial findings from the Global Hidradenitis Suppurativa COVID-19 Registry

2021· article· en· W3201603123 on OpenAlexafffundabout
Haley B. Naik, Raed Alhusayen, John W. Frew, Sandra Guilbault, Nancy K. Hills, John R Ingram, Margaret V. Kudlinski, Michelle A. Lowes, Angelo Valerio Marzano, Maia Paul, Bente Villumsen, Christine A. Yannuzzi

Bibliographic record

VenueJournal of the American Academy of Dermatology · 2021
Typearticle
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institutes of HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoUniversity of California, San FranciscoUniversity of TorontoNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCardiff UniversityUniversity of New South WalesSunnybrook Research Institute
KeywordsHidradenitis suppurativaMedicineCoronavirus disease 2019 (COVID-19)Logistic regressionDermatologyComorbidityInternal medicineDisease

Abstract

fetched live from OpenAlex

To the Editor: Hidradenitis suppurativa (HS) patients may be at increased risk of severe COVID-19 and poor outcomes due to comorbidities and biologic treatment.1 COVID-19 cases in HS patients were reported in the Global Hidradenitis Suppurativa COVID-19 Registry (https://hscovid.ucsf.edu/) from April 5, 2020, to February 2, 2021.1 Eligible cases had confirmed diagnosis of HS by a health care provider (HCP) or screening questions and COVID-19 diagnosis by an HCP. Comparisons were performed using the Fisher's exact or Pearson χ2 test.

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)0.003
metaresearch head score (Gemma)0.041
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.327
Teacher spread0.298 · 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

Citations13
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueJournal of the American Academy of DermatologySame topicHidradenitis Suppurativa and TreatmentsFrench-language works237,207