POSTOPERATIVE OUTCOMES OF MINIMALLY INVASIVE VERSUS OPEN CRANIOTOMY IN PATIENTS WITH INTRACRANIAL TUMORS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: The comparative efficacy and safety of minimally invasive craniotomy (MIC) versus open craniotomy (OC) for brain tumor resection remains a pivotal clinical question. While MIC techniques aim to reduce surgical morbidity, a comprehensive synthesis of high-quality evidence is needed to guide surgical decision-making. Objective: This systematic review aims to compare postoperative recovery, complication rates, and functional outcomes between MIC and OC in patients undergoing resection of intracranial tumors. Methods: A systematic review was conducted following PRISMA guidelines. PubMed, Scopus, Web of Science, and the Cochrane Library were searched for randomized controlled trials and comparative observational studies published between 2019-2024. Two independent reviewers screened studies, extracted data, and assessed risk of bias using the Cochrane RoB 2 and Newcastle-Ottawa tools. Results: Eight studies (n=1,823 patients) were included. MIC was associated with a significant reduction in estimated blood loss (mean difference: 150-275 mL, p<0.05) and length of hospital stay (reduction of 1.5-3.2 days, p<0.01) compared to OC. Complication rates, particularly for surgical site infection, were consistently lower in the MIC group. Crucially, there was no significant difference in the rate of gross total resection between the two approaches. Conclusion: For appropriately selected intracranial tumors, minimally invasive craniotomy demonstrates superior perioperative outcomes compared to open craniotomy, including reduced blood loss, shorter hospitalization, and fewer complications, without compromising the extent of tumor resection. These findings support the selective use of MIC, though further high-quality randomized trials are warranted to strengthen the evidence base.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".