SUBJECTIVE MEMORY EVALUATION BEFORE AND AFTER TEMPORAL LOBE EPILEPSY SURGERY
Bibliographic record
Abstract
Subjective memory (SM), a self-evaluation of memory, in contrast to objective memory (OM) measured by neuropsychological testing, is less well studied in patients with epilepsy. We assessed SM before and after temporal lobectomy. The Frequency of Forgetting 10 scale (FOF-10), developed to evaluate SM in dementia, was given before and one year after temporal lobectomy. Reliability and validity for use in epilepsy were first assessed. Measures of depression (CES-D) and neuroticism (PANAS) were done before and after surgery as well as complete neuropsychological assessment of OM. Correlation analysis between FOF-10 results and all the other variables was implemented. In 48 patients the FOF-10 was reliable and valid showing high internal consistency in all items (Cronbach's alpha >0.82) and high reproducibility (p<0.01). The FOF-10 also correlated with the memory assessment clinics self rating scale (MAC-S) (p<0.01). FOF-10 scores improved or were unchanged postoperatively in 28 patients (58%) and worsened in 20 (42%). The FOF-10 did not significantly correlate with memory scores from neuropsychological testing but did correlate with perceived word finding difficulty (p<0.001) and postoperative depression (p<0.05). A reduction in number of antiepileptic drugs (AEDs) after surgery distinguished those with improved postoperative SM. No correlation was found between SM and neuroticism, side of surgery or number of seizures. The FOF-10 is a brief and reliable measure of subjective memory in patients with epilepsy. Perceived memory impairment reflects more emotional state, language problems and quantity of AEDs than actual defects in memory function. These results would potentially be useful in presurgical counselling and management of memory issues after temporal lobe surgery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".