Three-Dimensional Printing Guide Plate-Guided Surgery Versus Computed Tomography–Guided Surgery for Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Computed tomography-guided intracerebral hematoma puncture is associated with issues such as significant localization errors and the need for multiple punctures. This study conducted a meta-analysis to systematically evaluate the differences in safety and accuracy between 3D printing guide plate localization and CT localization techniques in intracerebral hematoma evacuation procedures, providing evidence for primary care hospitals to adopt precise puncture techniques. METHODS: A systematic search was conducted across 12 databases, including PubMed, the Cochrane Library, Web of Science, Embase, Scopus, and the China National Knowledge Infrastructure (CNKI). The quality of studies was assessed using the Newcastle-Ottawa Scale or the Methodological Index for Non-Randomized Studies, depending on the study design. Data analysis was performed using statistical software. RESULTS: A comprehensive analysis of 28 clinical studies involving 2319 patients revealed that 3D printing guide plate localization technology offers significant advantages over CT localization in intracerebral hematoma puncture treatment, including higher puncture accuracy ( P ˂ 0.05), better postoperative neurological function recovery (postoperative Glasgow Coma Scale scores, P < 0.05), superior clinical efficacy ( P ˂ 0.05), and higher single-puncture success rates ( P ˂ 0.05). Although there were no significant differences between the two groups in terms of long-term outcomes (Glasgow Outcome Scale, P = 0.81), hematoma evacuation rate ( P = 0.06), and complication incidence ( P = 0.92), 3D printing technology significantly reduced mortality ( P ˂ 0.05) and hospitalization costs ( P ˂ 0.05). CONCLUSIONS: Current evidence suggests that 3D printing plate localization technique is a safer, more precise, and cost-effective approach for intracerebral hematoma puncture, especially applicable in primary care hospitals.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".