Impact of the 2021 north american winter storms on children with epilepsy
Bibliographic record
Abstract
Purpose: In February 2021 a series of winter storms caused power outages for nearly 10 million people in the United States, Northern Mexico and Canada. In Texas, the storms caused the worst energy infrastructure failure in state history, leading to shortages of water, food and heat for nearly a week. Impacts on health and well-being from natural disasters are greater in vulnerable populations such as individuals with chronic illnesses, for example due to supply chain disruptions. We aimed to determine the impact of the winter storm on our patient population of children with epilepsy (CWE). Methods: We conducted a survey of families with CWE that are being followed at Dell Children's Medical Center in Austin, Texas. Results: Of the 101 families who completed the survey, 62% were negatively affected by the storm. Twenty-five percent had to refill antiseizure medications during the week of disruptions, and of those needing refills, 68% had difficulties obtaining the medications, leading to nine patients-or 36% of those needing a refill-running out of medications and two emergency room visits because of seizures and lack of medications. Conclusions: Our results demonstrate that close to 10% of all patients included in the survey completely ran out of antiseizure medications, and many more were affected by lack of water, heat, power and food. This infrastructure failure emphasizes the need for adequate disaster preparation for vulnerable populations such as children with epilepsy for the future.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".