Discriminative Utility of Transcranial Magnetic Stimulation-Derived Markers of Cortical Excitability for Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND: Several guidelines currently recommend acute diffusion weighted imaging (DWI) for the detection of ischemia in transient ischemic attack (TIA). However, DWI hyperintensities resolve early and only 30%-50% with clinically defined TIA show acute DWI positivity. A recent meta-analysis reported an unexplained 7-fold variation in DWI positivity in TIA across studies, concluding that DWI does not provide a consistent basis for defining ischemia. Intracortical excitability, measured using transcranial magnetic stimulation (TMS), has previously been shown to be altered after TIA and associated with ABCD2 scores; however, whether altered cortical excitability is associated with clinical and DWI-based definitions of TIA remains unclear. METHODS: Individuals with TIA symptoms (N = 23; mean age = 61 ± 12) were prospectively recruited and underwent DWI and paired-pulse TMS. Multivariate linear regression was used to estimate associations between TMS-derived excitability thresholds, and clinical TIA diagnosis, and imaging-based evidence of cerebral ischemia (DWI positivity). Area under the curve (AUC) analyses was used to compare the discriminability of TMS-derived thresholds and clinical TIA diagnoses. RESULTS: Thresholds for intracortical inhibition in the TIA-unaffected hemisphere were significantly associated with the clinical diagnosis of TIA. No associations between TMS-derived thresholds and DWI positivity were observed. TMS thresholds showed low-moderate discriminability and values differed by age (65+) and sex. CONCLUSIONS: In this small sample, TMS-derived markers of intracortical excitability were associated with clinical TIA diagnoses but not DWI positivity. Our results provide preliminary evidence for the potential discriminative utility of TMS for the diagnosis of TIA and highlight the need for future work in larger cohorts.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".