The Adaptation of Pediatric Exercise Testing Programs to the Coronavirus/COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Response to the coronavirus/COVID-19 pandemic has resulted in several initiatives that directly impact hospital operations. There has been minimal information on how COVID-19 has affected exercise testing in pediatric patients. DESIGN: A web-based survey was designed and sent to pediatric exercise testing laboratories in the United States and Canada. Questions were designed to understand the initial and ongoing adaptations made by pediatric exercise testing laboratories in response to COVID-19. Results were analyzed as frequency data. RESULTS: There were responses from 42% (35/85) of programs, with 68% (23/34) of laboratories discontinuing all exercise testing. Of the 23 programs that discontinued testing, 15 (65%) are actively working on triage plans to reopen the exercise laboratory. Personal protective equipment use include gloves (96%; 25/26), surgical masks (88%; 23/26), N-95 masks (54%; 14/26), face shields (69%; 18/26), and gowns (62%; 16/26). Approximately 47% (15/32) of programs that typically acquire metabolic measurements reported either ceasing or modifying metabolic measurements during COVID-19. Additionally, 62% (16/26) of the programs that previously obtained pulmonary function testing reported either ceasing or modifying pulmonary function testing. Almost 60% of respondents expressed a desire for additional guidance on exercise laboratory management during COVID-19. CONCLUSIONS: Pediatric exercise testing laboratories largely closed during the early pandemic, with many of these programs either now open or working on a plan to open. Despite this, there remains heterogeneity in how to minimize exposure risks to patients and staff. Standardization of exercise testing guidelines during the COVID-19 pandemic may help reduce some of these differences.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".