Bibliographic record
Abstract
The reviews contained in the 2021 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 in training programs who contributed reviews with their mentors.The Editors selected the journals to be reviewed on the basis of 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 2020 to 2021 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; British Medical Journal; Clinical and Experimental Allergy; Clinical Infectious Diseases; European Respiratory Journal; International Archives of Allergy and Immunology; JAMA; JAMA Pediatrics; Journal of Allergy and Clinical Immunology; Journal of Allergy and Clinical Immunology: In Practice; Journal of Asthma; Journal of Clinical Immunology; Journal of Pediatric Gastroenterology and Nutrition; Journal of Pediatrics; Lancet; New England Journal of Medicine; Pediatrics; Pediatric Allergy and Immunology; Pediatric Dermatology; Pediatric Pulmonology; and 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); Timothy Andrews, MD (Arnold, MD); Marcella Aquino, MD (Providence, RI); James R. Banks, MD (Arnold, MD); Theresa A. Bingemann, MD (Rochester, NY); J. Andrew Bird, MD (Dallas, TX); Jeffrey Chambliss, MD (Dallas, TX); Jennifer Dantzer, MD (Baltimore, MD); Carla M. Davis, MD (Houston, TX); Karla L. Davis, MD (Honolulu, HI); Lisa R. Forbes-Satter, MD (Houston, TX); James E. Gern, MD (Madison, WI); Alan B. Goldsobel, MD (San Jose, CA); Ruchi Gupta, MD, MPH (Chicago, IL); Vivian Hernandez-Trujillo, MD (Miami, FL); Angela Duff Hogan, MD (Norfolk, VA); John Kelso, MD (San Diego, CA); Kirsten M. Kloepfer, MD (Indianapolis, IN); Mary V. Lasley, MD (Seattle, WA); Susan Laubach, MD (San Diego, CA); Harvey L. Leo, MD (Ann Arbor, MI); Mitchell R. Lester, MD (Norwalk, CT); Todd A. Mahr, MD (La Crosse, WI); Elizabeth C. Matsui, MD (Austin, TX); Jordan S. Orange, MD, PhD (New York, NY); Grace T. Padron, MD (Miami, FL); Christopher P. Parrish, MD (Dallas, TX); Michael Pistiner, MD (Boston, MA); Christopher Randolph, MD (Waterbury, CT); Melinda M. Rathkopf, MD (Anchorage, AK); Marcus Shaker, MD, MS (Lebanon, NH); Scott H. Sicherer, MD (New York, NY); Elinor Simons, MD (Toronto, ON, Canada); David R. Stukus, MD (Columbus, OH); Pooja Varshney, MD (Austin, TX); Girish Vitalpur, MD (Indianapolis, IN); Luke A. Wall, MD (New Orleans, LA); Julie Wang, MD (New York, NY); Kelli W. Williams, MD, MPH (Charleston, SC); Paul Williams, MD (Seattle, WA); Elizabeth L. Wisner, MD (New Orleans, LA); Robert A. Wood, MD (Baltimore, MD); 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.424 | 0.386 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".