The prognostic value of magnetic resonance imaging in moderate and severe traumatic brain injury: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is a devastating condition with significant long-term mortality and morbidity. Despite current need for objective indicators to guide initial decision-making, few reliable acute phase prognostic factors have been identified. Early magnetic resonance imaging (MRI) has been investigated as a prognostic tool, but uncertainty remains in both its discriminative predictive value and which acute phase lesion patterns correlate with long-term outcome. METHODS: We will conduct a systematic review of observational cohort studies and randomized controlled trials of adult moderate or severe TBI patients who underwent MRI in the acute phase after trauma. A high sensitivity search strategy will be employed in MEDLINE, EMBASE, BIOSIS, and Cochrane CENTRAL to identify citations. Two reviewers will independently screen all identified references for eligibility and extract data into a standardized form. Data will be collected on study design, baseline demographics, trauma characteristics, magnetic resonance (MR) technical specifications, lesion patterns, and outcomes as related to acute MRI imaging. If meta-analysis is possible, quantitative data for each outcome will be pooled per type of lesion pattern using random effects models and expressed as Mantel-Haenszel relative risks in order to determine the prognostic value of lesions detected on acute MRI and their strength as discriminatory predictors of long-term outcome. Statistical heterogeneity will be evaluated with the I (2) statistics, and risk of bias and reporting quality will be assessed with standardized scales. Subgroup analyses are planned as a function of TBI severity, MRI-timing post-TBI, MRI field strength, MRI sequence, timing of outcome assessment, and risk of bias. DISCUSSION: We expect significant clinical heterogeneity, as eligible studies will likely encompass different periods in evolving MRI technology in addition to significant variability of image sequence protocols and timing of acquisition between centers. Based on existing studies in TBI, we expect lesions detected in the brainstem to be of significant predictive value as MRI is particularly sensitive for imaging the brain's posterior fossa. Our systematic review will allow clinicians to more accurately interpret MRI in the context of determining prognosis for moderate and severe TBI patients and inform researchers in this domain to improve the methodology of future studies. SYSTEMATIC REVIEW REGISTRATION: Prospero CRD42015017074.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.035 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".