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Record W4233265063 · doi:10.1177/247553031218a00405

Abstracts from the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) Trainees Symposium

2012· article· en· W4233265063 on OpenAlexaboutno aff

Bibliographic record

VenuePsoriasis Forum · 2012
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsPsoriatic arthritisMedicinePresentation (obstetrics)PsoriasisFamily medicineLibrary scienceMedical educationDermatologySurgery

Abstract

fetched live from OpenAlex

On June 26, 2012, more than 30 fellows and trainees from around the world presented abstracts at the Trainees Symposium at the GRAPPA (Group for Research and Assessment of Psoriasis and Psoriatic Arthritis) Annual Meeting in Stockholm, Sweden. The symposium was developed as an opportunity for trainees and fellows in Rheumatology and Dermatology to develop their research skills through presentation and discussion of their work. The topics of the poster presentations ranged widely, reflecting many aspects of GRAPPA's work streams. Subjects included comorbidities, ethnicity in Psoriatic arthritis, nail disease, screening tools, incidence and prevalence studies, and translational research. Before the meeting, senior GRAPPA members ranked the abstracts, and the top 6 were invited to discuss their finding with a 15-minute slide presentation. The others were invited to speak for 5 to 7 minutes from posters based on their findings. This year, the 6 selected to give oral presentations were: Alex Nigg (Germany), Neha Garg (USA), Deepak Jadon (UK), Mary Ann Johnson (USA), Agnes Szentpetery (Ireland) and Zahi Touma (Canada). Professor Christopher Ritchlin, M. D., M.P.H., of the University of Rochester School of Medicine (New York) founded the GRAPPA Trainees Symposium in 2008. It has been a popular addition to the GRAPPA Annual Meeting ever since, with 15 trainees participating in 2008, 19 in 2009 and increased to 32 in 2012. Here we present the abstracts submitted by these trainees and fellows in 2012.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.353
Teacher spread0.306 · 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 teacher head, 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
Published2012
Admission routes1
Has abstractyes

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