Design of trials in lacunar stroke and cerebral small vessel disease: review and experience with the LACunar Intervention Trial 2 (LACI-2)
Bibliographic record
Abstract
Cerebral small vessel disease (cSVD) causes lacunar stroke (25% of ischaemic strokes), haemorrhage, dementia, physical frailty, or is 'covert', but has no specific treatment. Uncertainties about the design of clinical trials in cSVD, which patients to include or outcomes to assess, may have delayed progress. Based on experience in recent cSVD trials, we reviewed ways to facilitate future trials in patients with cSVD.We assessed the literature and the LACunar Intervention Trial 2 (LACI-2) for data to inform choice of Participant, Intervention, Comparator, Outcome, including clinical versus intermediary endpoints, potential interventions, effect of outcome on missing data, methods to aid retention and reduce data loss. We modelled risk of missing outcomes by baseline prognostic variables in LACI-2 using binary logistic regression.Imaging versus clinical outcomes led to larger proportions of missing data. We present reasons for and against broad versus narrow entry criteria. We identified numerous repurposable drugs with relevant modes of action to test in various cSVD subtypes. Cognitive impairment is the most common clinical outcome after lacunar ischaemic stroke but was missing more frequently than dependency, quality of life or vascular events in LACI-2. Assessing cognitive status using Diagnostic and Statistical Manual for Mental Disorders Fifth Edition can use cognitive data from multiple sources and may help reduce data losses.Trials in patients with all cSVD subtypes are urgently needed and should use broad entry criteria and clinical outcomes and focus on ways to maximise collection of cognitive outcomes to avoid missing data.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".