LITTing up Gliomas—Is the Future Bright?
Bibliographic record
Abstract
Background: Laser interstitial thermal therapy (LITT) represents an attractive therapeutic strategy for several intracranial pathologies; however, there is a paucity of literature regarding its efficacy for the treatment of gliomas. Methods: MEDLINE, EMBASE, Scopus, and Web of Science were searched from inception until March 19, 2021. Studies specifically relating to the use of LITT in treatment of glioma were eligible for inclusion. A meta-analysis of means was performed to assess the progression-free survival (PFS) and overall survival (OS) following LITT and descriptive statistics relating to patients undergoing LITT were collated and a meta-analysis of proportions was also performed to assess the rate of complications. Results: In total, 17 studies were included for the meta-analysis, comprising 401 patients with 408 gliomas of which 88 of 306 (28.8%) were grade 1 or 2 and 218 of 306 (71.2%) were grade 3 or 4. Of these, 256 of 408 (62.8%) were primary presentation and 152 of 408 (37.2%) were recurrent. The pooled mean OS was 13.58 months (95% confidence interval [CI] 9.77-17.39) and the PFS was 4.96 months (95% CI 4.19-5.72). The OS and PFS of recurrent glioblastoma were 12.4 months (95% CI 9.61-16.18) and 4.84 months (95% CI 0.23-9.45), respectively. Complications occurred in 114 of 411 (24%; 95% CI 14-41), of which 44 (11%) were transient deficits. Conclusions: There is an increasing body of evidence demonstrating the use of LITT in the surgical management of deep-seated gliomas in patients of poor performance status. However, further studies are required to interrogate the clinical effectiveness of LITT in the setting of gliomas as well as assessing the survival benefit versus standard treatment alone.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".