Cost‐utility analysis of magnetoencephalography used to inform intracranial electrode placement in patients with drug resistant epilepsy: a model based analysis
Bibliographic record
Abstract
OBJECTIVES: We estimated the cost-effectiveness of adding magnetoencephalography to a standard assessment for epilepsy surgery consisting of neuropsychology, magnetic resonance imagining, scalp electroencephalography, video electroencephalography and intracranial electroencephalography, in the capacity of informing intracranial electroencephalography electrode placement. METHODS: We used Microsoft Excel (2007) to construct a decision model. Discounted costs and quality adjusted life years are aggregated to calculate incremental cost-effectiveness ratios. Sensitivity analyses are conducted to assess robustness of findings. RESULTS: Our base case analysis yielded a result of $14 300 per quality adjusted life year gained. A total of 82.7% of probabilistic sensitivity analysis iterations resulted in incremental cost-effectiveness ratios below $100 000 in 2014 Canadian dollars. CONCLUSIONS: Our findings demonstrate that the inclusion of Magnetoencephalography in the assessment for epilepsy surgery in the capacity of informing intracranial electroencephalography electrode placement is likely not cost saving but does represent a reasonable allocation of resources from a value for money perspective.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".