Diagnostic Evaluation in Patients with Intractable Epilepsy and Normal Findings on MRI: A Decision Analysis and Cost-Effectiveness Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patients with focal intractable epilepsy and normal MR imaging findings frequently undergo further diagnostic tests to localize the epileptogenic zone. The aim of this study was to determine the cost-effective diagnostic strategy that will identify the epileptogenic zone in patients with suspected focal intractable epilepsy and normal MR imaging findings by using decision analysis. MATERIALS AND METHODS: A Markov decision model was constructed by using sensitivities and specificities of test strategies, seizure outcomes following surgical and medical treatment, cost, utilities, probabilities, and standardized mortality ratios. We compared 6 diagnostic test strategies: PET, ictal SPECT, and MEG individually; and combinations of PET+SPECT, PET+MEG, and SPECT+MEG. The outcomes measured were health care costs, QALY, and ICER. One-way and probabilistic sensitivity analyses were conducted to adjust for uncertainties in model parameters. RESULTS: The preferred strategies were PET+MEG and SPECT. The health care cost of the baseline strategy (PET+MEG) was $95,612 with 16.30 QALY gained. SPECT cost $97,479 with 16.45 QALY gained and an ICER of $12,934/QALY gained compared with those in PET+MEG. One-way sensitivity analyses showed that the decisions of the model were sensitive to variations in sensitivity and specificity of the test strategies. Probabilistic sensitivity analysis showed that when the willingness to pay was <$10,000, PET+MEG was the favored strategy, but the favored strategy changed to SPECT when the willingness to pay was >$10,000. CONCLUSIONS: PET+MEG and SPECT were the preferred strategies in the base case. The choice of test was dependent on the sensitivity and specificity of test strategies and willingness to pay. Further study with a larger sample size is needed to obtain better estimates of sensitivity and specificity of diagnostic tests.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".