Prediction of good neurological outcome after return of circulation following paediatric cardiac arrest: A systematic review and meta-analysis
Bibliographic record
Abstract
AIM: To evaluate the ability of blood-biomarkers, clinical examination, electrophysiology, or neuroimaging, assessed within 14 days from return of circulation to predict good neurological outcome in children following out- or in-hospital cardiac arrest. METHODS: Medline, EMBASE and Cochrane Trials databases were searched (2010-2023). Sensitivity and false positive rates (FPR) for good neurological outcome (defined as either 'no, mild, moderate disability or minimal change from baseline') in paediatric survivors were calculated for each predictor. Risk of bias was assessed using the QUIPS tool. RESULTS: Thirty-five studies (2974 children) were included. The presence of any of the following had a FPR < 30% for predicting good neurological outcome with moderate (50-75%) or high (>75%) sensitivity: bilateral reactive pupillary light response within 12 h; motor component ≥ 4 on the Glasgow Coma Scale score at 6 h; bilateral somatosensory evoked potentials at 24-72 h; sleep spindles, and continuous cortical activity on electroencephalography within 24 h; or a normal brain MRI at 4-6d. Early (≤12 h) normal lactate levels (<2mmol/L) or normal s100b, NSE or MBP levels predicted good neurological outcome with FPR rate < 30% and low (<50%) sensitivity. All studies had moderate to high risk of bias with timing of measurement, definition of test, use of multi-modal tests, or outcome assessment heterogeneity. CONCLUSIONS: Clinical examination, electrophysiology, neuroimaging or blood-biomarkers as individual tests can predict good neurological outcome after cardiac arrest in children. However, evidence is often low quality and studies are heterogeneous. Use of a standardised, multimodal, prognostic algorithm should be studied and is likely of added value over single modality testing.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".