Abstracts from the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GR APP A) Trainees Symposium
Bibliographic record
Abstract
On July 11, 2014, 27 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 New York City. 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. Subjects included comorbidities, nail disease, screening tools, incidence and relevance studies, and translational research. Before the meeting, senior GRAPPA members ranked the abstracts, and the top four were invited to present their findings orally 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 four selected to give oral presentations were Drs. Deepak Jadon (UK), Will Tillett (UK), Lihi Eder (Canada/Israel), and Agnes Szentpetery (Ireland). For the first time, the 2014 GRAPPA Annual Meeting and Fellows Symposium included a cohort of fellows from the Spondyloarthritis Treatment and Research Network (SPARTAN). Four SPARTAN fellows shared the podium with the GRAPPA oral presenters, and another dozen SPARTAN fellows presented posters alongside the GRAPPA poster presenters. Christopher Ritchlin, M.D., M.P.H., of the University of Rochester School of Medicine, founded the GRAPPA Trainees Symposium in 2008. It has been a popular addition to the GRAPPA annual meeting ever since. Here we present a selection of the abstracts submitted by the GRAPPA trainees and fellows from 2014.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".