Quality Assurance of Spirometry in a Population-Based Study –Predictors of Good Outcome in Spirometry Testing
Bibliographic record
Abstract
BACKGROUND: The assurance of high-quality spirometry testing remains a challenge. METHODS: Spirometry training consisted of standardized coaching followed by certification for 35 spirometry-naïve and 9 spirometry-experienced research assistants. Spirometry was performed before and after bronchodilator (BD) in random population samples of 5176 people aged 40 years and older from 9 sites in Canada. using the hand-held EasyOne spirometer (ndd Medical Technologies Inc., Andover, MA, USA). Pulmonary function quality assurance with over reading was conducted centrally in Vancouver: spirograms were reviewed and graded according to ATS/ERS standards with prompt feedback to the technician at each site. Descriptive statistics were calculated for manoeuvre acceptability and repeatability variables. A logistic regression model was constructed for the predictors of spirometry quality success. RESULTS: 95% of test sessions achieved pre-determined quality standards for back extrapolated volume (BEV), time to peak flow (PEFT) and end of test volume (EOTV). The mean forced expiratory time (FET) was 11.2 seconds. Then, 90% and 95% of all manoeuvres had FEV1 and FVC that were repeatable within 150 ml and 200 ml respectively. Test quality was slightly better for post-BD test sessions compared with pre-BD for both groups of research assistants. Independent predictors of acceptable test quality included participant characteristics: female sex, younger age, greater BD responsiveness; but not study site or prior experience in completing spirometry by the technologist. CONCLUSIONS: Good quality spirometry tests are attainable in large multicenter epidemiological studies by trained research assistants, irrespective of their prior experience in spirometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".