The use of induced sputum in clinical trials
Bibliographic record
Abstract
As asthma is considered to be an inflammatory disorder of the airways, it seems logical to include an assessment of this inflammatory process as an outcome measure in clinical trials. Biopsy studies illustrate that clinical or lung function characteristics such as symptoms, peak flow variability or degree of airway responsiveness do not consistently correlate with histological alterations. Therefore, these clinical indices cannot be seen as accurate markers of airway inflammation 1–3. Conversely, repeated bronchoscopic sampling is not feasible in largescale clinical studies. Hence there is interest in a relatively noninvasive but direct marker of airway inflammation. Analysis of induced sputum seems to meet these criteria. Provided proper precautions are taken, induction of sputum is safe, even in patients with more severe asthma 4, 5. In addition, sputum cell counts, particularly eosinophil counts, have been well validated in terms of responsiveness to intervention. It has been argued that, in comparison with other noninvasive markers of inflammation, induced sputum offers the most balanced assessment of the degree of inflammation, being more responsive to intervention than serum eosinophil cationic protein, yet not as oversensitive as exhaled nitric oxide 6–8. As for any outcome measure, when including induced sputum in a clinical trial, specific features of sputum analysis need to be taken into account when designing the study: 1) origin of sputum; 2) methodological aspects; 3) selection of subjects; and 4) power calculations. The induced sputum technique samples the inflammatory cells and soluble markers present in the airway lumen of the bronchial tree, which, although reflective of, does not represent an identical situation to the local inflammatory process in the mucosa. This probably explains the poor correlation between the cellular composition of biopsy samples and sputum, bronchial wash or bronchoalveolar lavage 9–11. Therefore, …
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 | 0.259 | 0.414 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".