Delayed Recognition of Initial Stroke in Children: Need for Increased Awareness
Bibliographic record
Abstract
OBJECTIVE: The goal was to identify the delays involved in diagnosing pediatric arterial ischemic stroke (AIS), a major cause of morbidity and death in children. METHODS: Neonates (<or=28 days of age) and children with a first presentation of radiologically confirmed AIS between June 1993 and January 2006 were identified retrospectively. The time to diagnosis of AIS (ie, time from clinical onset to radiologic confirmation) was calculated, and factors influencing stroke diagnosis were reviewed. RESULTS: A total of 107 patients (19 neonates and 88 children) with a diagnosis of AIS were identified. The median time to AIS diagnosis was 87.9 hours for neonates, significantly longer than 24.8 hours for children (P = .0002). Sixty-nine percent of the children with AIS demonstrated a likely cardioembolic cause, and 51 (58%) of the 88 children were inpatients at the time of stroke. The inpatients were seen by a physician more quickly (P < .01) and received a diagnosis of AIS sooner (P < .01). Seventy-six (86%) of the 88 children had a focal neurologic deficit when first seen by a physician. Physicians documented a diagnosis/differential diagnosis for 44 (50%) of 88 children, and they documented a suspicion of AIS for only 23 (26%) of 88 children. The presence of seizures or focal signs was not associated with a quicker time to stroke confirmation. CONCLUSIONS: The considerable delays in the diagnosis of pediatric AIS are most likely related to the lack of awareness of stroke among medical staff members, despite risk factors and focal signs at presentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".