Systematic review and meta-analysis of standard vs selective temporal lobe epilepsy surgery
Bibliographic record
Abstract
OBJECTIVE: To compare standard anterior temporal lobectomy (ATL) with selective amygdalohippocampectomy (SAH) for postoperative seizure control in temporal lobe epilepsy (TLE). METHODS: We searched MEDLINE and Embase using Medical Subject Headings and keywords related to ATL and SAH. We included original research that directly compared seizure outcomes in patients undergoing SAH or ATL for TLE. A fixed-effect model was used to derive a pooled risk ratio (RR) for either an Engel Class I (free of disabling seizures) or a composite of an Engel Class I and II (rare disabling seizures) outcome. RESULTS: Of 4,675 abstracts initially identified by the search, 65 were reviewed as full text. Thirteen studies containing data from 8 countries (5 continents) met our inclusion criteria. Eleven studies comprising 1,203 patients demonstrated that participants were statistically more likely to achieve an Engel Class I outcome after ATL compared with SAH (risk ratio 1.32, 95% confidence interval [CI] 1.12-1.57; p < 0.01). The summary risk difference of 8% (95% CI 3%-14%) translates to a number needed to treat of 13 (95% CI 7-33) for 1 additional patient to achieve an Engel Class I outcome after ATL. The result remained significant when 2 studies that contained fewer than 15 participants in at least 1 arm were excluded and in analyses restricted to hippocampal sclerosis. CONCLUSIONS: Standard ATL confers an improved chance of achieving freedom from disabling seizures in patients with TLE. Improved seizure freedom must be balanced against the neuropsychological impact of each procedure. A randomized controlled trial is justified.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".