Development of Quality Assurance and Quality Control Guidelines for Respiratory Oscillometry in Clinic Studies
Bibliographic record
Abstract
BACKGROUND: The guidelines to conduct and interpret conventional pulmonary function (PFT) tests are frequently reviewed and updated. However, the quality assurance and quality control (QA/QC) guidelines for respiratory oscillometry testing remain limited. QA/QC guidelines are essential for oscillometry to be used as a diagnostic pulmonary function test (PFT) in a clinical setting. METHODS: We developed a QA/QC protocol shortly after oscillometry was introduced in our laboratory as part of a clinical study. The first clinical study began after the research personnel completed 3 h of combined didactic and hands-on training and establishment of a standard operating protocol (SOP) for oscillometry testing. All oscillometry tests were conducted using the initial SOP protocol from October 17, 2017, to April 6, 2018. At this time, the first QA/QC audit took place, followed by revisions to the SOP, the addition of a QA/QC checklist, and the development of a 12-h training program. A second audit of oscillometry tests was conducted from April 9, 2018, to June 30, 2019. Both audits were completed by a registered cardiopulmonary technologist from the Toronto General Pulmonary Function Lab. RESULTS: The first audit evaluated 197 paired oscillometry-PFT tests and found 10 tests (5.08%) to be invalid, with a coefficient of variation > 15%. The second audit examined 1,930 paired oscillometry-PFT tests; only 3 tests (0.16%) were unacceptable, with a coefficient of variation > 15%. Improvement in QA/QC was significantly better compared to the first audit ( P < .001). CONCLUSIONS: Although oscillometry requires minimal subject cooperation, application of the principles that govern the conduct and application of a PFT are important for ensuring that oscillometry testing is performed according to acceptability and reproducibility. Specifically, the inclusion of a SOP, a proper training program, a QA/QC checklist, and regular audits with feedback are vital to ensure that oscillometry is conducted accurately and precisely.
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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 | 0.635 | 0.594 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".