International consensus on lung function testing during the COVID-19 pandemic and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it