Visualization of atrophy of medial temporal lobes and the septal nuclei in patients with transient ischaemic attack and controls
Bibliographic record
Abstract
Transient ischaemic attack (TIA) is associated with increased risk of cognitive decline and dementia as early as one-year post-event. Regional brain atrophy measurements may predict future cognitive decline. Aims: 1) To determine whether Medial Temporal Atrophy (MTA) scores and interseptal distance (ISD) measurements are greater in patients with TIA compared to controls; and 2) To determine whether MTA and ISD predicts cognitive change one year after TIA. Baseline demographic, vascular risk factors, structural imaging and cognitive tests scores were compared between 103 Patients with TIA and 103 age-and-sex-matched controls from the Predementia Neuroimaging of Transient Ischaemic Attack (PREVENT) Study. MTA was assessed using the Schelten's Scale, and ISD was calculated as the distance between the septal nucleus of each hemisphere. Multiple linear regression models were used to evaluate how MTA and ISD related to cognitive change after adjusting for covariates. Patients with TIA had larger ISD measurements (1.4 mm [SD=1.2] vs. 0.9 mm [SD=1.0]); p <.001) and higher right/left MTA scores (both p <0.05) compared to controls. At baseline, controls performed significantly better on the RAVLT (total recall), BVMT (total and delayed recall) and the Trail Making Task (A and B) compared to patients with TIA. However, at one-year follow-up there was no evidence of decline in the patients with TIA compared with controls. Higher MTA and ISD scores were not associated with cognitive decline. Patients with TIA had higher MTA scores and ISD measurements than controls, but neither were predictors of cognitive decline at one year. Future studies with longer follow-up periods will be required to determine whether higher MTA scores and ISD predict risk of cognitive decline in patients with TIA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".