Early economic evaluation of MRI-guided laser interstitial thermal therapy (MRgLITT) and epilepsy surgery for mesial temporal lobe epilepsy
Bibliographic record
Abstract
BACKGROUND: MRI-guided laser interstitial thermal therapy (MRgLITT) is a new minimally invasive treatment for temporal lobe epilepsy (TLE), with limited effectiveness data. It is unknown if the cost savings associated with shorter hospitalization could offset the high equipment cost of MRgLITT. We examined the cost-utility of MRgLITT versus surgery for TLE from healthcare payer perspective, and the value of additional research to inform policy decision on MRgLITT. METHODS: We developed a microsimulation model to evaluate quality adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER) of MRgLITT versus surgery in TLE, assuming life-time horizon and 1.5% discount rate. Model inputs were derived from the literature. We conducted threshold and sensitivity analyses to examine parameter uncertainties, and expected value of partial perfect information analyses to evaluate the expected monetary benefit of eliminating uncertainty on probabilities associated with MRgLITT. RESULTS: MRgLITT yielded 0.08 more QALYs and cost $7,821 higher than surgery, with ICER of $94,350/QALY. Influential parameters that could change model outcomes include probabilities of becoming seizure-free from disabling seizures state and returning to disabling seizures from seizure-free state 5 years after surgery and MRgLITT, cost of MRgLITT disposable equipment, and utilities of disabling seizures and seizure-free states of surgery and MRgLITT. The cost-effectiveness acceptability curve showed surgery was preferred in more than 50% of iterations. The expected monetary benefit of eliminating uncertainty for probabilities associated with MRgLITT was higher than for utilities associated with MRgLITT. CONCLUSIONS: MRgLITT resulted in more QALYs gained and higher costs compared to surgery in the base-case. The model was sensitive to variations in the cost of MRgLITT disposable equipment. There is value in conducting more research to reduce uncertainty on the probabilities and utilities of MRgLITT, but priority should be given to research focusing on improving the precision of estimates on effectiveness of MRgLITT.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".