A few good cells: basophils in the resolution of ARDS
Bibliographic record
Abstract
Extract With an estimated incidence of 86.2 cases per 100 000 people per year and unabated mortality rates of up to 43%, acute respiratory distress syndrome (ARDS) continues to constitute the major cause of mortality and morbidity in critical care [1]. The magnitude of this global health issue became strikingly evident during the past coronavirus disease 2019 (COVID-19) pandemic, when the global prevalence of ARDS induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection amounted to 32.2%, with mortality rates reaching 56% [2]. Despite major advances in our understanding of the underlying cellular and molecular pathomechanisms at single-cell resolution [3] and the identification of different, hyper- and hypo-inflammatory, subphenotypes in ARDS patient populations [4], therapeutic interventions remain limited to physiological measures, such as low tidal volume ventilation, prone positioning and fluid restriction. In contrast, all efforts to design pharmacological and/or mechanistic interventions have so far proven futile.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".