P07.06.B The Impact of Using Intraoperative Ultrasound on Surgical Resection of High-Grade Glioma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Despite operative and adjuvant therapies, high-grade glioma (HGG) remains incurable, with the extent of surgical resection being one of the modalities that can improve patient survival. Enabling maximal safe and minimising post-operative neurological morbidity is a key aim of surgical resection. Numerous intraoperative surgical adjuncts are used at surgery and intraoperative ultrasound (IoUS), is one such adjunct. IoUS is a cost-effective, easy to use, repeatable surgical adjunct, safe for the patient and potentially available in all centres. Although it’s commonly used, no up to date systematic review exists collating and quantifying the level of evidence, delineating its impact on the extent of surgical resection. Material and Methods A systematic review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The study was registered with the PROSPERO database (CRD42022300034). Keywords across Medline/PubMed and Embase between 1996 and November 2021 were used. We included articles with adult supratentorial, histopathologically confirmed HGG patients aimed for resection, evaluating the correlation of IoUS use and gross-total resection (GTR). Meta-analyses were conducted according to the statistical heterogeneity between the studies using the Open Meta Analyst software. Results 2942 articles were identified of which 16 were qualitative assessed and 10 used for quantitative meta-analysis. In qualitative assessment, a mean 4.63/8 Newcastle-Ottawa-Scale score was found for studies with no cohorts (no use of IoUS) and a mean score of 6/9, for studies including exposed versus non-exposed cohorts. The RCT was of moderate quality according to the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) tool. A pooled analysis across 10 studies of HGG aimed for resection with the use of IoUS, led to GTR achieved in 168/365 cases, resulting in an overall GTR rate of 51.1% (95% CI, 33.9%-68.3%, p<0.001), with great heterogeneity across studies (93.02% p<0.001). In a subgroup meta-analysis of 3 studies of HGG aimed for complete resection only, GTR was achieved in 43/62 cases, yielding a 72.7% GTR rate (95% CI 41.6%-100%, p<0.001) with significant heterogeneity across studies (I2 92.1%, p<0.001). In 4 case-controlled studies, a total of 43.6% (48/110) GTR rate was achieved when IoUS was used versus 24.7% (65/263) when IoUS was not used, resulting in an odds ratio = 2.009 (95% CI 1.157-3.490, p <0.001) for achieving GTR. Conclusion The meta-analysis showed a high GTR rate (72.7%) when HGG were aimed for complete resection and a two-fold probability of achieving GTR when IoUS is used than not used.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".