Bibliographic record
Abstract
OBJECTIVE: Asthma is the most common medical emergency in children. It is associated with significant morbidity and mortality rates and poses a tremendous societal burden worldwide. Management of the acute attack involves a stepwise approach that includes beta-agonist and steroid therapy, the mainstay of emergency treatment. Most patients will respond to this regime and can be discharged from the emergency department. Failure to respond to treatment necessitates hospital admission and sometimes admission to the intensive care unit (ICU). Management in the ICU involves intensification of pharmacologic therapy, including nonstandard therapies, in an attempt to avoid intubation and ventilation. When needed, mechanical ventilatory support can be rendered fairly safe with little morbidity if the likely cardiorespiratory physiologic derangements are appreciated and if appropriate ventilatory strategies are used. In the past two decades, the availability of newer potent medications and changes in approach to monitoring and ventilatory strategies have resulted in a decrease in ICU morbidity and mortality rates. Research endeavors are presently underway to further characterize the underlying mechanisms of the disease and are likely to lead to novel therapies. This article reviews the approach to management of acute severe asthma.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.026 |
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".