Prediction of dementia using CT imaging in stroke (PRODUCTS)
Bibliographic record
Abstract
INTRODUCTION: A better understanding of who will develop dementia can inform patient care. Although MRI offers prognostic insights, access is limited globally, whereas CT-imaging is readily available in acute stroke. We explored the prognostic utility of acute CT-imaging for predicting dementia. PATIENTS AND METHODS: We included stroke or transient ischaemic attack (TIA) survivors from participating stroke centres in Scotland. Acute CT-scans were rated using ordinal scales for neurodegenerative and cerebrovascular changes (old infarcts, white matter lesions (WMLs), medial temporal lobe atrophy (MTA), and global atrophy (GA)) and combined together to a 'brain-frailty' score. Dementia status was established at 18-months following stroke or TIA. RESULTS: Among 195 participants, 33% had dementia after 3 years of follow-up. High brain-frailty score (⩾2/4) correlated with higher risk of dementia (HR (95% CI) 6.02 (1.89-19.21)). As individual predictor, severe MTA was most strongly associated with dementia (adjusted HR (95% CI) 2.09 (1.07-4.08)). Other predictors associated with dementia included older age, higher prestroke morbidity (mRS), WMLs, and GA. Integrated in a prediction model with clinical parameters, prestroke mRS, cardiovascular disease, GA, MTA and Abbreviated-Mental-Test were the strongest predictors of dementia (c-statistic: 0.77). DISCUSSION AND CONCLUSION: Increased brain-frailty, and its individual components (WMLs, MTA, and GA) are associated with a higher risk of dementia in participants with stroke. Combining clinical and brain-frailty parameters created a moderate dementia prediction model but added little value over clinical parameters in combination with cognitive testing. CT-based brain-frailty may provide better prognostic insights when cognitive testing isn't feasible and for identifying highest-risk individuals for dementia prevention trials to increase trial efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".