International consensus on lung function testing during the COVID-19 pandemic and beyond
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) has negatively affected the delivery of respiratory diagnostic services across the world due to the potential risk of disease transmission during lung function testing. Community prevalence, reoccurrence of COVID-19 surges and the emergence of different variants of SARS-CoV-2 have impeded attempts to restore services. Finding consensus on how to deliver safe lung function services for both patients attending and for staff performing the tests are of paramount importance. This international statement presents the consensus opinion of 23 experts in the field of lung function and respiratory physiology balanced with evidence from the reviewed literature. It describes a robust roadmap for restoration and continuity of lung function testing services during the COVID-19 pandemic and beyond. Important strategies presented in this consensus statement relate to the patient journey when attending for lung function tests. We discuss appointment preparation, operational and environmental issues, testing room requirements including mitigation strategies for transmission risk, requirement for improved ventilation, maintaining physical distance and use of personal protection equipment. We also provide consensus opinion on precautions relating to specific tests, filters, management of special patient groups and alternative options to testing in hospitals. The pandemic has highlighted how vulnerable lung function services are and forces us to re-think how long-term mitigation strategies can protect our services during this and any possible future pandemic. This statement aspires to address the safety concerns that exist and provide strategies to make lung function tests and the testing environment safer when tests are required.
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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 | 0.058 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".