The memory assessment clinics scale for epilepsy (MAC-E): A brief measure of subjective cognitive complaints in epilepsy
Bibliographic record
Abstract
Objective The aim of this study was to conduct item reduction of the Memory Assessment Clinics Self-Rating Scale (MAC-S) to create a briefer measure that can be used to quickly evaluate subjective memory complaints in patients with epilepsy. Method: A total of 1333 adults with focal epilepsy completed the original 49-item MAC-S. The sample was randomly split into three subsamples, and a series of analyses (i.e. exploratory factor analysis, confirmatory factor analysis, and item response theory analyses) was conducted to identify an alternative factor structure, with a reduced number of items. A panel of 5 neuropsychologists independently reviewed the final model to assess appropriateness of each individual item as well as the factor loadings and overall factor structure. Final factor titles were subsequently decided as a group. Results: Five factors were identified: Attention, Working Memory, Retrieval, Semantic Memory, and Episodic Memory. The length of the MAC-S was reduced from 49 to 30 items, with items being removed because they failed to load onto any of the factors substantially, or because of poor item discrimination or threshold levels. Conclusions: The Memory Assessment Clinics Scale for Epilepsy (MAC-E), is an updated, brief measure of subjective memory functioning that can be used to efficiently assess relevant, every-day memory abilities in patients with epilepsy within both clinical and research settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".