Dupilumab, severe asthma airway responses, and SARS‐CoV‐2 serology
Bibliographic record
Abstract
Dr Bhalla is supported by the Frederick E. Hargreave fellowship in Airway Diseases. Ms Radford, Ms Kjarsgaard, and Dr Nazy do not have any disclosures. Dr Mukherjee is supported by investigator award from Canadian Institutes of Health Research and Canadian Allergy, Asthma, and Immunology Foundation. She has received grants from Methapharm Specialty Pharmaceuticals and honorarium from AZ. Dr Bowdish is supported by a Canada Research Chair in Aging & Immunity. She has received honoraria from Pfizer, Astra Zeneca, and Abbvie. Dr Nair is supported by the Frederick E. Hargreave Teva Innovation Chair in Airway Diseases. He has received honoraria from AZ, Sanofi, Teva, Merck, Novartis, and Equillium, and his university has received research grants from AZ, Teva, Sanofi, Novartis, BI, and Methapharm.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".