Bibliographic record
Abstract
PURPOSE OF REVIEW: This article focuses on two novel asthma therapies - antibiotics and a procedure, bronchial thermoplasty. The challenges of identifying which treatment would best help an individual patient can be addressed by use of noninvasive measurements to define their asthma. RECENT FINDINGS: Asthma is heterogeneous. Methods can be applied that define different phenotypes. We can now obtain a more detailed description of physiological changes, for example with bronchial provocation, and inflammatory changes, for example with exhaled nitric oxide or sputum cell analysis, in patients with airway symptoms. These measurements help define disease mechanisms and are especially informative when patients do not respond to standard therapy. Furthermore, detailed phenotyping may help identify who is most likely to benefit from newly developed, more specific therapies ranging from antagonists of individual mediators, for example anti-tumor necrosis factor-alpha or anti-immunoglobulin E, to interventions that directly address structural determinants of asthma, for example bronchial thermoplasty. SUMMARY: Asthma treatment is evolving beyond the current cornerstones of bronchodilation, leukotriene antagonism and corticosteroids. This change will be propelled by a more detailed description of individual patients' disease that will enable customization of treatment, and the development of specific interventions that modify disease mechanisms, including airway remodelling.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".