Abstract WP174: Incidence Of Pediatric And Neonatal Cerebral Venous Sinus Thrombosis In The United States (US)
Bibliographic record
Abstract
Background: Prior pediatric CVT incidence estimates range from 0.4-0.7 per 100,000 per year but no US specific incidence has previously been reported. We examined incidence of CVT hospitalization within a large US pediatric database. Methods: We evaluated incidence of neonatal and childhood CVT using the 2012 and 2016 Healthcare Cost and Utilization Project Kids’ Inpatient Database (KID), a large-scale administrative dataset providing estimates of US hospital inpatient stays for children. Patients under 20 years of age were examined to match US census population data. All children with discharge diagnoses of CVT were queried using ICD-9 (2012) and ICD-10 (2016) codes. Survey weights were applied to estimate total national discharges adjusting for KID’s sampling technique. Population incidence was calculated from 2012 and 2016 US vital statistics data for live births for neonates and census data for other ages. Results: Table 1 summarizes CVT-related discharges by sex and age group. Overall annual incidence for CVT related hospitalization was 4.37 (95% CI 3.72-5.02) and 5.51 (95% CI 4.78-6.24) per 100 000 births for neonates in 2012 and 2016 respectively and 1.18 (95% CI 1.11-1.26) and 1.58 (95% CI 1.49-1.66) per 100 000 other children under 20 years old in 2012 and 2016. Mortality was 3.4% and 3.53% for all CVT discharges compared to 0.34% and 0.36% for all US pediatric discharges in 2012 and 2016. The overall incidence of CVT related hospitalization was higher in 2016 than 2012 for all age groups and was higher for males until the 15-19 age group in both years. Conclusion: Higher incidence of CVT hospitalizations in 2016 versus 2012 is similar to trends seen in adults. Unlike adults, more CVT hospitalizations occurred among males until the 15-19 age group. The current analysis is limited by inability of available national data to differentiate between hospitalizations due to new CVT presentation and re-admission.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".