Systematic review and network meta-analysis of resective surgery for mesial temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of anterior temporal lobectomy (ATL) versus selective amygdalohippocampectomy (SAH) on seizure-free outcome in patients with temporal lobe epilepsy, using both direct and indirect evidence from the literature. METHODS: MEDLINE, Embase and Cochrane databases were searched for original research articles and systematic reviews comparing ATL versus SAH, and ATL or SAH versus medical management (MM). The outcome was seizure freedom at 12 months of follow-up or longer. Direct pairwise meta-analyses were conducted, followed by a random-effect Bayesian network meta-analysis (NMA) combining direct and indirect evidence. RESULTS: Twenty-eight articles were included (18 compared ATL vs SAH, 1 compared ATL vs SAH vs MM, 8 compared ATL vs MM, and 1 compared SAH vs MM). Direct pairwise meta-analyses showed no significant differences in seizure-free outcome of ATL versus SAH (OR 1.14, 95% CI 0.93 to 1.39; p=0.201), but the odds of seizure-free outcome were higher for ATL versus MM (OR 29.16, 95% CI 10.44 to 81.50; p<0.00001), and SAH versus MM (OR 28.42, 95% CI 10.17 to 79.39; p<0.00001). NMA also showed that the odds of seizure-free outcome were no different in ATL versus SAH (OR 1.15, 95% credible interval (CrI) 0.84-1.15), but higher for ATL versus MM (OR 27.22, 95% CrI 15.38-27.22), and SAH versus MM (OR 23.57, 95% CrI 12.67-23.57). There were no significant differences between direct and indirect comparisons (all p>0.05). CONCLUSION: Direct evidence, indirect evidence and NMA did not identify a difference in seizure-free outcome of ATL versus SAH.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".