Thoracic Endovascular Aortic Repair versus Optimal Medical Treatment in Patients with Type B Intramural Hematoma: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: We intended to study the effect of thoracic endovascular aortic repair (TEVAR) and optimal medical treatment (OMT) on type B intramural hematoma (BIMH). METHODS: We searched PubMed, EMbase, Cochrane Library, and China National Knowledge Infrastructure databases that compared TEVAR and OMT in patients with BIMH. Two authors independently assessed the risk of bias using the Newcastle-Ottawa Scale. The rate ratio (RR) and 95% confidence interval were used to calculate the outcome. The primary endpoints were aortic-related death and regression/resolution. Secondary endpoints were all-cause death, progression to dissection, and secondary intervention. RESULTS: Eight observational studies were included in the analysis. TEVAR reduced aortic-related death (RR 0.22, 95% CI 0.08-0.56, P = 0.002, I² = 24%) and promoted hematoma regression/resolution (RR 1.48, 95% CI 1.05-2.10, P <0.05, I² = 71%) compared to OMT. Moreover, TEVAR was associated with a reduction in progression to dissection (RR 0.32, 95% CI 0.13-0.81, P <0.02, I² = 39%) and secondary intervention (RR 0.18, 95% CI 0.09-0.37, P <0.00001, I² = 38%) compared to OMT. However, all-cause death has no significant difference between the two groups (RR 0.45, 95% CI 0.17-1.19, P = 0.11, I² = 58%). CONCLUSIONS: The results of this meta-analysis suggested that TEVAR is an effective treatment for BIMH, which can delay the progression of intramural hematoma and promotes regression/resolution. More research about indications of TEVAR is still needed.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".