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.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| 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".