Delays to care in infantile epileptic spasms syndrome: Racial and ethnic inequities
Bibliographic record
Abstract
OBJECTIVE: Non-Hispanic (NH) Black children are less likely to receive a standard treatment course for infantile epileptic spasms syndrome (IESS) than White/NH children at pediatric tertiary care epilepsy centers in the United States. However, if inequities exist in time to diagnosis is unknown. Diagnostic delays as little as 1 week can be associated with worse developmental outcomes. METHODS: Diagnostic delays were evaluated in a retrospective cohort of 100 children with new onset IESS between January 2019 and May 2022. RESULTS: Children with Black, Indigenous, and People of Color (BIPOC) caregivers were more likely to experience clinically significant delays in referral from first provider to neurologist, when compared to White/NH children, even after controlling for other demographic and clinical variables (odds ratio = 4.98, confidence interval = 1.24-19.94, p = .023). SIGNIFICANCE: Disproportionate diagnostic delays place BIPOC children at risk of adverse developmental and epilepsy outcomes. Further interventional prospective and qualitative studies are needed to address inequities in care.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".