The prevalence and risk factors of posttraumatic cerebral infarction in patients with traumatic brain injury: a systematic review and meta-analysis
Bibliographic record
Abstract
Posttraumatic cerebral infarction (PTCI) is a serious complication of traumatic brain injury (TBI), and the prevalence and risk factors of PTCI in TBI patients are in dispute. We systematically searched the literature in the PubMed, Embase, and Cochrane library up to October 2021 to identify studies on the prevalence and risk factors of PTCI in patients with TBI. The quality of observational studies was assessed by the Newcastle–Ottawa scale tool. Random-effects model was conducted. The Higgins` I2 statistic was used to measure heterogeneity between trials. Moreover, sensitive analyses were conducted to assess whether the pooled result was credible and robust. Eleven studies (3696 total TBI patients) were included. The pooled prevalence of PTCI in TBI patients was 14% (95% CI, 0.11–0.17; I2 = 83.1%). Sensitive analyses showed that the pooled prevalence of PTCI was 13% (95% CI, 0.10–0.15; I2 = 69.2%) by omitting Su et al. The prevalence of PTCI was associated with a lower Glasgow Coma Scale (GCS) score (OR, 0.33; 95% CI, 0.14–0.77; I2 = 99.2%), pupillary dilation (OR, 4.73; 95% CI, 4.30–5.19; I2 = 85.6%), abnormal PT (OR, 1.16; 95% CI,1.05–2.47; I2 = 99.2%), hematoma location (OR, 1.16; 95% CI,1.05–2.47; I2 = 99.2%) and hematoma volume (OR, 1.16; 95% CI,1.05–2.47; I2 = 99.2%). Whereas hypotensive shock, duraplasty, cerebral herniation, and thrombocytopenia were not statistically associated with PTCI. Lower GCS, pupillary dilation, abnormal PT, hematoma location, and hematoma volume were risk factors for PTCI. Considering some limitations, the conclusion of our study should be interpreted with caution.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".