Diagnostic pitfalls in paediatric ischaemic stroke
Bibliographic record
Abstract
Diagnosing ischaemic stroke and determining its cause is difficult in children. Both are important for selection of treatment and prediction of outcome. This study explored the diagnostic changes that lead to a delay in the correct diagnosis of paediatric stroke. Case histories of 45 children with ischaemic stroke (31 males, 14 females; median age 6y; age range 2mo-16y) were retrospectively reviewed. The initial clinical diagnosis, based on the interpretation of presenting symptoms, was compared with the final aetiological stroke diagnosis after completion and review of diagnostic work-up. The type of diagnostic change, consequent time delay until correct diagnosis, reasons for change of diagnosis, and alterations to management were evaluated. Twenty-four diagnostic changes were identified; 19 in 'primary stroke diagnosis' (symptoms initially not attributed to stroke), and five in 'aetiological diagnosis' (incorrect initial determination of type or cause of stroke). The median interval between initial and final diagnosis was 7 days (3h-2y). The change in diagnosis led to therapeutic alterations in 17 patients. Risk factors for childhood stroke differ from those in adults. Stroke is frequently not recognized as the cause of the child's symptoms, and the correct determination of stroke aetiology takes time. We recommend that children with stroke be evaluated in a centre with expertize, using standardized diagnostic protocols and careful follow-up.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".