Proceedings of the 10th Pediatric Dermatology Research Alliance (<scp>PeDRA</scp>) Annual Conference
Bibliographic record
Abstract
The 10th Pediatric Dermatology Research Alliance (PeDRA) Annual Conference occurred November 3-5, 2022 in Bethesda, Maryland. This conference was the first in-person PeDRA conference after 2 years of a virtual format due to COVID-19. Fittingly, given the effects of the pandemic, the conference theme was "Reimagining Community." The conference included presentations and panel sessions on finding individual and collective purpose, leveraging community in pursuit of a shared goal, and creating a community of resources in collaboration with NIH. The goal of this meeting was to connect clinicians, basic scientists, patients, patient advocates, and industry partners. The reimagined community of pediatric dermatology research is a synergistic space for all members to better understand, prevent, treat, and cure dermatologic diseases and conditions in children. This two-and-a-half-day conference with over 300 attendees featured educational seminars including a keynote address, didactic lecture and panel sessions, skill-building workshops, 13 topic-specific breakout sessions, and an interactive poster session where 108 active and finished research projects could be discussed.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.021 |
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.
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".