Can Allergen Immunotherapy Improve Antiviral Immunity in Patients with Allergic Asthma?
Bibliographic record
Abstract
endobronchial ultrasound to access an intracardiac sarcoma (Irfan and colleagues, pp.e73-e74).Look out also for the page highlighting our emerging young investigators who have done so much to make this and all our prior issues so stellar, and who are the future of the specialty.Above all, the patient education and information section reminds us that we all exist to serve patients.Finally, huge thanks are due to the AJRCCM family, Associate Editors and the Editorial Board, the authors and editorialists who have sent us such fantastic manuscripts, and the unseen army of reviewers, without whose hard grunt work we could not function.If you are in Washington, come along to the ceremony honoring our top reviewers and vow to be one of them in 2024.And, of course, a huge shout out to the editorial team, without whose largely unseen work in the engine room the Journal would not exist.And to the ATS members and beyond: keep sending us your best work, as we seek to drive the Journal to ever greater heights going forward.Vivat, vivat ATS and AJRCCM!
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".