Insights in Pediatric Rheumatology: 2021
Bibliographic record
Abstract
We are now entering the third decade of the 21st Century, and, especially in the last years, the achievements made by scientists have been exceptional, leading to major advancements in the fast-growing field of Pediatrics.Frontiers has organized a series of Research Topics to highlight the latest advancements in research across the field of Pediatrics, with articles from the Associate Members of our accomplished Editorial Boards. This editorial initiative of particular relevance, led by Prof. Rolando Cimaz, Specialty Chief Editor of the Pediatric Cardiology section, together with Prof. Marco Cattalini, is focused on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives in the field of Pediatric Rheumatology.The Research Topic solicits brief, forward-looking contributions from the editorial board members that describe the state of the art, outlining, recent developments and major accomplishments that have been achieved and that need to occur to move the field forward. Authors are encouraged to identify the greatest challenges in the sub-disciplines, and how to address those challenges.The goal of this special edition Research Topic is to shed light on the progress made in the past decade in the Pediatric Rheumatology field, and on its future challenges to provide a thorough overview of the field. This article collection will inspire, inform and provide direction and guidance to researchers in the field.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.054 |
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".