Abstracts from the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) Trainees Symposium
Bibliographic record
Abstract
On July 12, 2013, more than 30 fellows and trainees from around the world presented abstracts at the Trainees Symposium at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) annual meeting in Toronto, Canada. 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, nail disease, screening tools, incidence and prevalence studies, and translational research. Before the meeting, senior GRAPPA members ranked the abstracts, and the top six were invited to present their findings orally with a 15-minute slide presentation. The others were invited to speak for five to seven minutes from posters based on their findings. This year, the six selected to give oral presentations were Muhammad Haroon (Ireland), Will Tillett (UK), Mary Ann Johnson (U.S.A.), Agnes Szentpetery (Ireland), Amir Hadaad (Canada) and DoQuyen Huynh (U.S.A.). 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 31 abstracts submitted in 2013. Here we present a selection of the abstracts submitted by these trainees and fellows in 2013.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.089 | 0.045 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".