Predicting poor neurological outcomes following out-of-hospital cardiac arrest using neuron-specific enolase and neurofilament light chain in patients with and without haemolysis
Bibliographic record
Abstract
Abstract Aims Hypoxic-ischaemic brain injury following out-of-hospital cardiac arrest (OHCA) is a common complication and a major cause of death. Neuron-specific enolase (NSE) and neurofilament light chain (NfL) are released after brain injury and elevated concentrations of both are associated with poor neurological outcome. We explored the influence of haemolysis on the prognostic performance of NSE and NfL. Methods and results The study is based on post hoc analyses of a randomized, single-centre, double-blinded, controlled trial (IMICA), where comatose OHCA patients of presumed cardiac cause were included. Free-haemoglobin was measured at admission to quantify haemolysis. NSE and NfL were measured after 48 h to estimate the extent of brain injury. Montreal Cognitive Assessment score (MoCA) was assessed to evaluate neurocognitive impairments. Seventy-three patients were included and divided into two groups by the median free-haemoglobin at admission. No group differences in mortality or poor neurological outcome were observed. The high-admission free-haemoglobin group had a significantly higher concentration of NSE compared to the low-admission free-haemoglobin group (27.4 µmol/L vs. 19.6 µmol/L, P = 0.03), but no differences in NfL. The performance of NSE and NfL in predicting poor neurological outcome were high for both, but NfL was numerically higher [area under the ROC (AUROC) 0.90 vs. 0.96, P = 0.09]. Furthermore, NfL, but not NSE, was inversely correlated with MoCA score, R2 = 0.21, P = 0.006. Conclusion High free-haemoglobin at admission was associated with higher NSE concentration after 48 h, but, the performance of NSE and NfL in predicting poor neurological outcome among OHCA patients were good regardless of early haemolysis. Only elevated NfL concentrations were associated with cognitive impairments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".