Infection Control and Management of COVID-19: Challenges for Paediatric Tertiary Care Hospitals
Bibliographic record
Abstract
Abstract PURPOSE: To describe infection control measures and patient management at a tertiary children’s hospital in southern Germany during the COVID-19 pandemic.METHODS: Prospective, observational study of infection control measures, patient management, clinical and virologic data of paediatric patients treated at our hospital during the COVID-19 pandemic from February to May 2020. Infection control measures were documented beginning with preparation for the pandemic. All paediatric patients with suspected SARS-CoV-2 infection were prospectively included in the study.RESULTS: With local triage, restraint of patient admission and testing strategies implemented, healthcare capacity remained adequate and no healthcare-associated infections occurred. Workload in the paediatric emergency department significantly decreased following the lockdown of schools and kindergartens. 7 of 174 (4%) children with and 2 of 208 (1%) children without typical symptoms, respectively, were diagnosed with COVID-19 by PCR. Six out of nine inpatients treated for COVID-19 had underlying comorbidities, two were admitted to the intensive care unit. One patient died shortly after discharge.CONCLUSIONS: While COVID-19 generally causes mild disease in children, severe illness and fatal cases may occur, particularly among children with underlying diseases. Tertiary children’s hospitals may face challenges with treating potential high-risk patients during the pandemic. Thus, timely establishment of effective testing and triage strategies is crucial.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".