Multicenter Study of the Impact of COVID-19 Shelter-In-Place on Tertiary Hospital-based Care for Pediatric Neurologic Disease
Bibliographic record
Abstract
Objective To describe changes in hospital-based care for children with neurologic diagnoses during the initial 6 weeks following regional Coronavirus 2019 Shelter-in-Place orders. Methods This retrospective cross-sectional study of 7 US and Canadian pediatric tertiary care institutions included emergency and inpatient encounters with a neurologic primary discharge diagnosis code in the initial 6 weeks of Shelter-in-Place (COVID-SiP), compared to the same period during the prior 3 years (Pre-COVID). Patient demographics, encounter length, and neuroimaging and electroencephalography use were extracted from the medical record. Results 27,900 encounters over 4 years were included. Compared to Pre-COVID, there was a 54% reduction in encounters during Shelter-in-Place. COVID-SiP patients were younger (median 5 years vs 7 years). The incidence of encounters for migraine fell by 72%, and encounters for acute diagnoses of status epilepticus, infantile spasms, and traumatic brain injury dropped by 53%, 55%, and 56%, respectively. There was an increase in hospital length of stay, relative utilization of intensive care, and diagnostic testing (long-term electroencephalography, brain MRI, and head CT (all P<.01)). Conclusion During the initial 6 weeks of SiP, there was a significant decrease in neurologic hospital-based encounters. Those admitted required a high level of care. Hospital-based neurologic services are needed to care for acutely ill patients. Precise factors causing these shifts are unknown and raise concern for changes in care seeking of patients with serious neurologic conditions. Impacts of potentially delayed diagnosis or treatment require further investigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".