Conducting Respiratory Oscillometry in an Outpatient Setting
Bibliographic record
Abstract
Respiratory oscillometry is a different modality of pulmonary function testing that is increasingly used in a clinical and research setting to provide information regarding lung mechanics. Respiratory oscillometry is conducted through three acceptable measurements of tidal breathing and can be performed with minimal contraindications. Young children and patients who cannot perform spirometry due to cognitive or physical impairment can usually complete oscillometry. The main advantages of respiratory oscillometry are that it requires minimal patient cooperation and is more sensitive in detecting changes in small airways than conventional pulmonary function tests. Commercial devices are now available. Updated technical guidelines, standard operating protocols, and quality control/assurance guidelines have recently been published. Reference values are also available. We conducted oscillometry test audits before and after implementing a formal respiratory oscillometry training program and standard operating protocol. We observed improvement in the quality of tests completed, with a significant increase in the number of acceptable and reproducible measurements. The current paper outlines and demonstrates a standard operating protocol to conduct respiratory oscillometry in an outpatient setting. We highlight the key steps to ensuring acceptable and reproducible quality measurements according to the recommended European Respiratory Society (ERS) guidelines, as quality control is critical to measurement accuracies. Potential problems and pitfalls are also discussed with suggestions to resolve technical errors.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".