Incidence and Current Treatment Gaps in Pediatric Stroke and TIA: An Ontario-Wide Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Reported incidence rates of pediatric stroke and transient ischemic attack (TIA) range widely. Treatment gaps are poorly characterized. We sought to evaluate in -Ontario, the incidence and characteristics of pediatric stroke and TIA including care gaps and the predictive value of International Classification of Diseases (ICD) codes. METHODS: A retrospective chart review was conducted at 147 Ontario pediatric and adult acute care hospitals. Pediatric stroke and TIA cases (age < 18 years) were identified using ICD-10 code searches in the 2010/11 Canadian Institute for Health Information's Discharge Abstract Database (CIHI-DAD) and National Ambulatory Care Reporting System (NACRS) databases in the Ontario Stroke Audit. RESULTS: Among 478 potential pediatric stroke and TIA cases identified in the CIHI-DAD and NACRS databases, 163 were confirmed as cases of stroke and TIA during the 1-year study period. The Ontario stroke and TIA incidence rate was 5.9 per 100,000 children (3.3 ischemic, 1.8 hemorrhagic and 0.8 TIA). Mean age was 6.4 years (16% neonate). Nearly half were not imaged within 24 h of arrival in emergency and only 56% were given antithrombotic treatment. At discharge, 83 out of 121 (69%) required health care services post-discharge. Overall positive predictive value (PPV) of ICD-10 stroke and TIA codes was 31% (range 5-74%) and yield ranged from 2.4 to 29% for acute stroke or TIA event; code I63 achieved maximal PPV and yield. CONCLUSION: Our population-based study yielded a higher incidence rate than prior North-American studies. Important care gaps exist including delayed diagnosis, lack of expert care, and departure from published treatment guidelines. Variability in ICD PPV and yield underlines the need for prospective data collection and for improving the pediatric stroke and TIA coding processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".