Patterns of Clinical Use of Stereotactic Laser Ablation: Analysis of a Multicenter Prospective Registry
Bibliographic record
Abstract
BACKGROUND: Stereotactic laser ablation (SLA), also termed laser interstitial thermal therapy, is a minimally invasive procedure that is increasingly used in neurosurgery. We wished to examine how and whether SLA is changing the landscape of treatment options for neurosurgical patients. METHODS: Patients undergoing stereotactic laser ablation were prospectively enrolled in the Laser Ablation of Abnormal Neurological Tissue (LAANTERN) registry. Data from the first 100 enrolled patients are presented here. RESULTS: Clinical indications for SLA include treatment of primary intracranial tumors (48%; 81% being high-grade gliomas [HGGs]), brain metastases (BMs, 34%), epilepsy (16%), and other (2%). For HGGs, SLA was equally likely used for newly diagnosed (45%) or previously treated/recurrent lesions (55%, P = 0.54). By contrast, SLA was predominantly used as treatment for BMs in which radiation therapy/radiosurgery had failed (91%), with only 9% of SLAs performed as initial treatment for newly diagnosed lesions (P < 0.001). Of all SLAs performed, 45% of the procedures were in lieu of surgical resection, with 43% performed because the lesion was not accessible by conventional neurosurgical techniques. CONCLUSION: HGGs and BMs are the leading indications for SLA in the LAANTERN study. For HGGs, SLA is equally used in the presenting or previously treated/recurrent setting. For BMs, SLA is typically used in the recurrent setting. SLAs are equally likely to be performed for difficult-to-access lesions or in lieu of conventional open surgery.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".