Laser Ablation of Abnormal Neurological Tissue Using Robotic Neuroblate System (LAANTERN): Procedural Safety and Hospitalization
Bibliographic record
Abstract
BACKGROUND: Stereotactic laser ablation (SLA) has demonstrated potential utility for a spectrum of difficult to treat neurosurgical pathologies in multiple small and/or retrospective single-institutional series. Here, we present the safety profile of SLA of intracranial lesions from the Laser Ablation of Abnormal Neurological Tissue using Robotic NeuroBlate System (LAANTERN; Monteris Medical) multi-institutional, international prospective observational registry. OBJECTIVE: To determine the procedural safety of SLA for intracranial lesions. METHODS: Prospective procedural safety and hospitalization data from the first 100 treated LAANTERN patients was collected and analyzed. RESULTS: Mean age and baseline Karnofsky Performance Status (KPS) were 51(± 17) yr and 83(± 15), respectively. In total, 81.2% of patients had undergone prior surgical or radiation treatment. Most patients had a single lesion (79%) ablated through 1 burr hole (1.2 ± 0.7 per patient), immediately following a lesion biopsy. In total, >90% of the lesion was ablated in 72% of treated lesions. Average total procedural time was 188.2 ± 69.6 min, and average blood loss was 17.7 ± 55.6 ccs. The average length of intensive care unit (ICU) and hospital stays before discharge were 38.1 ± 62.7 h and 61.1 ± 87.2 h, respectively. There were 5 adverse events (AEs) attributable to SLA (5/100; 5%). After the procedure, 84.8% of patients were discharged home. There was 1 mortality within 30 d of the procedure (1/100; 1%), which was not attributable to SLA. CONCLUSION: SLA is a safe, minimally invasive procedure with favorable postprocedural ICU and hospital utilization profiles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".