Diagnostic utility of brain MRI in spontaneous intracerebral hemorrhage: A retrospective cohort study and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The diagnostic yield of brain Magnetic Resonance Imaging (MRI) in spontaneous intracerebral hemorrhage (ICH) is unclear. We performed both an independent single-center retrospective cohort study and a meta-analysis to assess the detection rate of secondary lesions on MRI in patients with spontaneous ICH. PATIENTS AND METHODS: In the retrospective cohort study, we examined 856 consecutive patients with spontaneous ICH. Brain MRI scans on admission and follow-up were assessed for secondary lesions. We also examined clinical and CT radiographic variables associated with secondary lesions in univariable analysis. In the meta-analysis we searched PubMed and EMBASE for articles investigating the secondary lesion detection rate on brain MRI in spontaneous ICH. RESULTS: Of the 856 patients with ICH, 481 (56%) had at least one brain BRI performed [70 ± 14 years, 270 (56% male)]. 462 (54%) had an admission MRI and 138 (16%) had both admission and follow-up MRIs. The detection rate of secondary lesions on admission MRIs was 24/462 (5.2%). 4/127 (3.1%) patients with a negative admission MRI had a lesion identified on follow-up MRI. No clinical or radiographic variables were associated with a secondary lesion on MRI using univariable analysis. The meta-analysis included five studies total (four identified in the PubMed and EMBASE searches and our cohort study) comprising 1147 patients with spontaneous ICH who underwent brain MRI. The pooled detection rate of secondary lesions was 11% (95% CI: 7-16). DISCUSSION AND CONCLUSION: No predictors of secondary lesion detection were identified in our cohort study. Prospective studies are required to better understand the diagnostic utility of MRI in spontaneous ICH.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.026 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".