Incidence of COVID-19 after pulmonary function tests: a retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: It has been proposed that exposure to pulmonary function tests (PFT) could be associated with a higher risk of viral transmission. The risk of the Coronavirus Disease 2019 (COVID-19) transmission after performing PFT is unknown. We aimed to assess the incidence of COVID-19 after a PFT at an academic teaching facility in Buenos Aires, Argentina. MATERIALS AND METHODS: We conducted a retrospective cohort study including all consecutive adult patients that performed PFT between April 1, 2020 and September 30, 2020. Patients with prior COVID-19 were excluded. We defined a 15-day time window to ascertain PFT related COVID-19. The primary outcome was ascertained by consulting a national database, which has information on all patients with nasopharyngeal swabs for SARS-CoV-2 in Argentina. RESULTS: We included 278 patients who performed a PFT. Fifty percent were women, the mean age was 54 years (SD 18), and the main comorbidities were obesity (31%), smoking (31%), hypertension (29%), and chronic lung disease (28%). The main indication for performing PFT was anesthetic preoperative risk assessment. Swabs were collected from 27 patients (10%). Twenty-two swabs (8%) were taken according to surgical protocols; five swabs (2%) were taken due to clinical suspicion of COVID-19, with only one testing positive. The cumulative incidence of COVID-19 after PFT was 0.36% (95% CI 0.01-20%). None of the technicians developed symptomatic disease. CONCLUSION: Given the right setting and strict adherence to international recommendations, the SARS-CoV-2 infection after having a PFT appears to be low, which follows that these procedures can be performed safely for both patients and staff.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".