Virtual mask fitting in pediatric patients during COVID-19: A case series
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has been an unprecedented threat to our health care system. Clinicians had to pivot and develop creative and timely "virtual" solutions to provide clinical care. Our aim was to develop a standardized approach to virtual "mask fitting" for children who are either being initiated or are already on existing long-term ventilation (LTV) at a pediatric hospital. CASE AND OUTCOMES: We present three cases involving the care of children who required mask fitting for noninvasive ventilation (NIV). LTV team consultations were delivered via videoconference or phone. With the guidance of the respiratory therapist (RT), the family caregiver (FC) took measurements on their child using a standardized clinical approach (developed by the LTV RTs). Based on the measurements, an appropriate mask was selected. Successful mask fit was based on patient/FC reports, as well as objective leak data obtained from the NIV download data. DISCUSSION: Virtual clinics used for managing patients in our LTV program were feasible and efficient resulting in improved workflow for the RTs and convenience for patients and FCs. Patients and FCs had significantly less pressure to attend in-person clinics and expressed high satisfaction in terms of their experience and importantly, meeting respiratory care needs. Within the context of COVID-19, remote patient education and intervention can be delivered effectively, while reducing the risk of exposure from in-person visits to hospital. CONCLUSION: A virtual/telemedicine program to manage pediatric patients requiring mask fitting for LTV was a feasible option during COVID-19.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".