Epilepsy in the elderly: temporal lobe atrophy present in many cases of epilepsy of unknown etiology (P6.294)
Bibliographic record
Abstract
OBJECTIVE : To evaluate the degree of temporal lobe atrophy (TLA) in elderly patients with new-onset epilepsy. BACKGROUND : Among elderly patients with newly diagnosed epilepsy, no etiology can be identified in 30 to 50[percnt] of cases, despite adequate investigation. Degenerative diseases, notably Alzheimer’s disease (AD), increase the risk of epilepsy, and are often underdiagnosed in routine clinical practice. DESIGN/METHODS : We retrospectively reviewed neuroimaging for presence of TLA, a biomarker for AD, on MRI (46[percnt]) or CT (93[percnt]) for 112 patients with epilepsy of unknown etiology. The subjects were part of a consecutive cohort of 322 patients >65 year-old with new-onset epilepsy evaluated at the Centre Hospitalier Universitaire de Sherbrooke (CHUS) between january 2001 and october 2010. TLA was assessed visually on axial images and graded on a 0-3 scale estimating sulcal widening and temporal horn ventricular enlargement. Measurement of the temporal horn radial width (THRW) was also acquired as a quantitative estimate of TLA. Comparison was made with a healthy elderly control group (n=31), non-epileptic AD patients (n=10) and demented epileptic individuals of the same cohort (n=26). RESULTS : Thirty-eight percent of elderly with new-onset epilepsy of unknown cause had significant temporal atrophy, using the dichotomised 0-3 scale (0-1 vs 2-3) or THRW. This was significantly more than the healthy control group (0[percnt]) after adjustment for age and sex (p=0.000). As expected, individuals of the same cohort with epilepsy attributed to dementia had higher prevalence of significant TLA using both THRW and the visual scale (70.4[percnt], p=0.026 and 76.9[percnt] p=0.021) as did patients with established AD (70[percnt] p=0.040 and 60[percnt] p=0.077). CONCLUSIONS : A considerable proportion of elderly with new-onset epilepsy of unknown cause exhibit temporal lobe atrophy on their brain imaging, which suggests that a degenerative disease such as AD could be underrecognized as a possible etiology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".