Applications of oscillometry in clinical research and practice
Bibliographic record
Abstract
Oscillometry is gaining in clinical use and while there is an increased interest in the technique, there is paucity in the understanding of its possibilities and limitations. Oscillometry has seen extensive use in research over several decades, but only recently is the technique being adopted in clinical practice; hence, there is a need to educate the novel users. The goal of the mini symposium arranged in San Diego in 2018 was to discuss the principles of oscillometry, showcase some of the recent ongoing research using this technique and to demonstrate how oscillometry may be used in clinical practice. It was concluded that oscillometry has several advantages over spirometry, most notably, with novel data being shown, its sensitivity allowing early detection of small airways disease not possible with spirometry and it can be used in subjects who have difficulties performing forced maneuvers such as preschool children, the elderly and subjects with handicaps. The site of respiratory pathology can be reflected by the various parameters generated by oscillometry and thus help with both disease diagnosis and localization. While the interpretation of oscillometry parameters and translating them into meaningful pathological correlates is still evolving, it is likely that oscillometry will soon be at the forefront of both pulmonary clinical practice and research.
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 | 0.083 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".