Predicting mood decline following temporal lobe epilepsy surgery in adults
Bibliographic record
Abstract
OBJECTIVE: To develop a model to predict the probability of mood decline in adults following temporal lobe resection for the treatment of pharmacoresistant epilepsy. METHODS: Variable selection was performed on 492 patients from the Cleveland Clinic using best subsets regression. After completing variable selection, a subset of variables was requested from four epilepsy surgery centers across North America (n = 100). All data were combined to develop a final model to predict postoperative mood decline (N = 592). Internal validation with bootstrap resampling was performed. A clinically significant increase in depressive symptoms was defined as a 15% increase in Beck Depression Inventory-Second Edition score and a postoperative raw score > 11. RESULTS: Fourteen percent of patients in the Cleveland Clinic cohort and 22% of patients in the external cohort experienced clinically significant increases in depressive symptoms following surgery. The final prediction model included six predictor variables: psychiatric history, resection side, relationship status, verbal fluency score, age at preoperative testing, and presence/absence of malformation of cortical development on magnetic resonance imaging. The model had an optimism-adjusted c-statistic of .70 and good calibration, with slight probability overestimation in higher risk patients. SIGNIFICANCE: Clinicians can utilize our nomogram via a paper tool or online calculator to estimate the risk of postoperative mood decline for individual patients prior to temporal lobe epilepsy surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".