Bibliographic record
Abstract
The reviews contained in the 2024 synopsis were written by Fellows of the American Academy of Pediatrics Section on Allergy and Immunology and fellows in allergy and immunology and pediatrics residents and fellows in training programs who contributed reviews with their mentors.The Editors selected the journals to be reviewed based on the likelihood that they would contain articles on allergy and immunology that would be of value and interest to the pediatrician. Each journal was assigned to a voluntary reviewer who was responsible for selecting articles and writing reviews of their articles. Only articles of original research were selected for review. Final selection of the articles to be included was made by the Editor.The 2023–2024 journals chosen for review were: Allergy; American Journal of Respiratory and Critical Care Medicine; Annals of Allergy, Asthma, and Immunology; Archives of Disease in Childhood; Clinical and Experimental Allergy; European Respiratory Journal; JAMA Dermatology; JAMA Pediatrics; Journal of Allergy and Clinical Immunology; Journal of Allergy and Clinical Immunology Global; Journal of Allergy and Clinical Immunology: In Practice; Journal of the American Academy of Dermatology; Journal of Asthma; Journal of Clinical Immunology; Journal of Immunology; Journal of Pediatric Gastroenterology and Nutrition; Journal of Pediatrics; Lancet; Lancet Child and Adolescent Health; Nature and Cell; New England Journal of Medicine; Pediatrics; Pediatric Allergy and Immunology; Pediatric Dermatology; Pediatric Pulmonology; Science and Science Translational Medicine.The Editor and the Section on Allergy and Immunology gratefully acknowledge the work of the reviewers and their trainees who assisted. The reviewers were: Stuart L. Abramson, MD, PhD, San Angelo, TX; Andrew Abreo, MD, New Orleans, LA; Sara Anvari, MD, Houston, TX; Timothy Andrews, MD, Arnold, MD; Marcella Aquino, MD, Providence, RI; Theresa A. Bingemann, MD, Rochester, NY; J. Andrew Bird, MD, Dallas, TX; Terri Brown-Whitehorn, MD, Philadelphia, PA; Jeffrey Chambliss, MD, Dallas, TX; Bradley E. Chipps, MD, Sacramento, CA; Timothy Chow, MD, Dallas, TX; Carla M. Davis, MD, Houston, TX; Karla L. Davis, MD, Honolulu, HI; Clinton Dunn, MD, Norfolk, VA; Alan B. Goldsobel, MD, San Jose, CA; Ruchi Gupta, MD, MPH, Chicago, IL; Vivian P. Hernandez-Trujillo, MD, Miami, FL; Angela Duff Hogan, MD, Norfolk, VA; Akilah Jefferson, MD, MSc, Little Rock, AR; John Kelso, MD; San Diego, CA; Stephanie Kubala, MD, Philadelphia, PA; Harvey L. Leo, MD, Ann Arbor, MI; Mitchell R. Lester, MD, Norwalk, CT; Jennifer Miller, MD, Houston TX; Lindsey Moore, DO, Norfolk, VA; Christopher P. Parrish, MD, Dallas, TX; Michael Pistiner, MD, Boston, MA; Melinda Rathkopf, MD, Atlanta, GA; Marcus Shaker, MD, MS, Lebanon, NH; Scott H. Sicherer, MD, New York, NY; Elinor Simons, MD, Toronto, ON; David R. Stukus, MD, Columbus, OH; Girish Vitalpur, MD, Indianapolis, IN; Julie Wang, MD, New York, NY; Kelli W. Williams, MD, MPH, Charleston, SC; Elizabeth L. Wisner, MD, New Orleans, LA; and Joyce Yu, MD, New York, NY.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.408 | 0.372 |
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".