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Record W2902865996 · doi:10.1111/exd.13849

Proceeding report of the Second Symposium on Hidradenitis Suppurativa Advances (<scp>SHSA</scp>) 2017

2018· article· en· W2902865996 on OpenAlexaffabout
Eran Shavit, Afsáneh Alavi, Falk G. Bechara, Richard G. Bennett, Marc Bourcier, Ricardo Cibotti, Steven Daveluy, John W. Frew, Amit Garg, Iltefat Hamzavi, Lauren Hoffman, Jenny Hsaio, Joslyn S. Kirby, Hadar Lev‐Tov, Erin Martinez, Robert G. Micheletti, Haley B. Naik, Aude Nassif, Cynthia L. Nicholson, Angie Parks‐Miller, Zarine S. Patel, Vincent Piguet, Mayur Ramesh, Barry I. Resnik, Christopher J. Sayed, Gregory S. Schultz, Aamir Siddiqui, Jerry Tan, Ximena Wortsman, Michelle A. Lowes

Bibliographic record

VenueExperimental Dermatology · 2018
Typearticle
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsUniversity of WindsorWomen's College HospitalUniversité de SherbrookeWindsor Clinical ResearchWestern UniversityUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsHidradenitis suppurativaMedicineSession (web analytics)DermatologyFoundation (evidence)GerontologyDiseaseWorld Wide WebPathologyComputer science

Abstract

fetched live from OpenAlex

The 2nd Annual Symposium on Hidradenitis Suppurativa Advances (SHSA) took place on 03-05 November 2017 in Detroit, Michigan, USA. This symposium was a joint meeting of the Hidradenitis Suppurativa Foundation (HSF Inc.) founded in the USA, and the Canadian Hidradenitis Suppurativa Foundation (CHSF). This was the second annual meeting of the SHSA with experts from different disciplines arriving from North America, Europe and Australia, in a joint aim to discuss most recent innovations, practical challenges and potential solutions to issues related in the management and care of Hidradenitis Suppurativa patients. The last session involved clinicians, patients and their families in an effort to educate them more about the disease.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0700.021

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 head0.016
GPT teacher head0.306
Teacher spread0.290 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations23
Published2018
Admission routes2
Has abstractyes

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