Predictive value of neuron-specific enolase for prognosis in patients with moderate or severe traumatic brain injury: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Prognosis is difficult to establish early after moderate or severe traumatic brain injury despite representing an important concern for patients, families and medical teams. Biomarkers, such as neuron-specific enolase, have been proposed as potential early prognostic indicators. Our objective was to determine the association between neuron-specific enolase and clinical outcomes, and the prognostic value of neuron-specific enolase after a moderate or severe traumatic brain injury. METHODS: We searched MEDLINE, Embase, The Cochrane Library and Biosis Previews, and reviewed reference lists of eligible articles to identify studies. We included cohort studies and randomized controlled trials that evaluated the prognostic value of neuron-specific enolase to predict mortality or Glasgow Outcome Scale score in patients with moderate or severe traumatic brain injury. Two reviewers independently collected data. The pooled mean differences were analyzed using random-effects models. We assessed risk of bias using a customized Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subgroup and sensitivity analyses were performed based on a priori hypotheses. RESULTS: 2 = 82%). We were unable to determine a clinical threshold value using the available patient data. INTERPRETATION: In patients with moderate or severe traumatic brain injury, increased neuron-specific enolase serum levels are associated with unfavourable outcomes. The optimal neuron-specific enolase threshold value to predict unfavourable prognosis remains unknown and clinical decision-making is currently not recommended until additional studies are made available.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it