SARS-CoV-2 infection in technology-dependent children: a multicenter case series
Bibliographic record
Abstract
Abstract Purpose: The objective of this study was to describe the clinical course and outcomes in children with technology-dependence (TD) hospitalized with SARS-CoV-2 infection.Methods: Seventeen pediatric hospitals (15 Canadian and one each in Iran and Costa Rica) included children up to 17 years of age admitted February 1, 2020, through May 31, 2021, with detection of SARS-CoV-2. For those with TD, data were collected on demographics, clinical course and outcome. Results: Of 691 children entered in the database, 42 (6%) had TD of which 22 had feeding tube dependence only, 9 were on supplemental oxygen only, 3 had feeding tube dependence and were on supplemental oxygen, 2 had a tracheostomy but were not ventilated, 4 were on non-invasive ventilation, and 2 were on mechanical ventilation prior to admission. Three of 42 had incidental SARS-CoV-2 infection. Two with end-stage underlying conditions were transitioned to comfort care and died. Sixteen (43%) of the remaining 37 cases required increased respiratory support from baseline due to COVID-19 while 21 (57%) did not. All survivors were discharged home.Conclusion: Children with TD appear to have an increased risk of COVID-19 hospitalization. However, in the absence of end-stage chronic conditions, all survived to discharge.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".