Cilostazol and isosorbide mononitrate for the prevention of progression of cerebral small vessel disease: Baseline data and statistical analysis plan for the Lacunar Intervention Trial-2 (LACI-2) (ISRCTN14911850)
Bibliographic record
Abstract
ABSTRACT Background Cerebral small vessel disease (SVD) causes lacunar strokes (25% of all ischaemic strokes), physical frailty and cognitive impairment and vascular and mixed dementia. There is no specific treatment to prevent progression of SVD. Methods The LACunar Intervention Trial-2 (LACI-2) is an investigator-initiated prospective randomised open-label blinded-endpoint (PROBE) phase II feasibility study assessing cilostazol and isosorbide mononitrate for preventing SVD progression. We aimed to recruit 400 patients with clinically-evident lacunar ischaemic stroke and randomised to cilostazol, isosorbide mononitrate, both or neither, in addition to guideline secondary ischaemic stroke prevention, in a partial factorial design. The primary outcome is feasibility of recruitment and adherence to medication; key secondary outcomes include: drug tolerability; recurrent vascular events, cognition and function at one year after randomisation; and safety (bleeding, falls, death). Data are number (%), and median [interquartile range]. Results The trial commenced on 5 th of February 2018 and ceased recruitment on 31 st of May 2021 with 363 patients randomised, with the following baseline characteristics: average age 64 [56.0, 72.0] years, female 112 (30.9%), stroke onset to randomisation 79.0 [27.0, 244.0] days, hypertension 267 (73.6%), median blood pressures 143.0 [130.0, 157.0]/83.0 [75.0, 90.0] mmHg, current smokers 67 (18.5%), educationally achieved end of school examinations (A-level) or higher 118 (32.5%), modified Rankin scale 1.0 [0.0, 1.0], National Institutes Health stroke scale (NIHSS) 1.0 (1.4), Montreal Cognitive Assessment 26.0 [23.0, 28.0] and total SVD score on brain imaging 1.0 [0.0, 2.0]. This publication summarises the baseline data and presents the statistical analysis plan. Summary The trial is currently in follow-up which will complete on 31 May 2022 with results expected in October 2022.
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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 | 0.061 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".