Improving spirometry testing by understanding patient preferences
Bibliographic record
Abstract
The American Thoracic Society and European Respiratory Society commissioned a task force to update the technical standards for spirometry testing with the aim of increasing the accuracy, precision and quality of spirometry measurements and improving the patient experience. To inform the task force with patient experiences, the European Lung Foundation, in collaboration with the task force, conducted an online survey in 10 languages between August and September 2018. There were 1760 respondents from 52 countries. The majority were adults (97.1%); the most common reasons for spirometry referral were diagnosis (35.0%) and management of an ongoing condition (60.1%). 53.2% reported regularly using inhalers. Respondents were very experienced with spirometry: 89.9% completed more than one test; 48% completed 10 or more tests. However, most reported not knowing what forced expiratory volume in 1 s (FEV1) means (59.4%) and only 39.6% knew their most recent FEV1; the exception was respondents with cystic fibrosis who reported much greater knowledge. Respondents rated as moderately or seriously problematic: being told to keep blowing when they felt nothing is coming out (31.4%), coughing (30.4%), tiredness (26.3%) and concern about shortness of breath (20.1%). Overall, respondents found spirometry to be acceptable; however, an important minority (17%) found it difficult. Patients want clear information before, during and after the test, including information on stopping medications. Operators have an important role in increasing the ease of patients and changes to the testing environment can increase patient comfort. Patients want access to their results and want to understand how they relate to their individual health.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".