3D-Printed Endoport vs. Open Surgery for Evacuation of Deep Intracerebral Hemorrhage
Bibliographic record
Abstract
ABSTRACT: Background: Large-sized clinical trials have failed to show an overall benefit of surgery over medical treatment in managing spontaneous intracerebral hemorrhages (ICH); less invasive techniques have shown to decrease brain injury caused by surgical manipulation in the standard open approach improving the clinical outcomes of patients. Thereby, we propose a low-cost 3D-printed endoport for a less invasive ICH evacuation. In this study, the authors compare the clinical outcomes of early surgical evacuation using a 3D-printed endoport vs. a standard open surgery (OS). Methods: A retrospective analysis was conducted comparing patients who underwent early evacuation of a deep hypertensive ICH through an endoport vs. OS at a single center from August 2017 to March 2019. Demographic, clinical, and radiologic data were reviewed. The primary outcomes were the 90-day post-stroke functional outcome and mortality. Results: A total of 36 patients were included. The two cohorts (18 endoport; 18 OS) showed no statistically significant differences in demographic, clinical, and radiologic characteristics, including median admission hemorrhage volume, Glasgow Coma Scale, and ICH scores. At 90-day post-stroke, 44% of patients in the endoport group and 17% in the OS group had a favorable functional outcome (mRS 0–3) (p = 0.039); moreover, the endoport group showed lower mortality (33% vs. 72%, p = 0.019). Conclusions: This study suggests that an endoport-assisted ICH evacuation may have better functional outcomes and lower mortality than OS. The proposed device could provide a safe, low-cost alternative for ICH’s surgical treatment. More rigorous research is hence needed to assess the potential benefits of this technique.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".