Statistical Analysis Plan for the Secondary Prevention by Structured Semi-Interactive Stroke Prevention Package in INDIA (SPRINT INDIA): A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Recurrent stroke is one of the major causes of death in stroke patients. Introducing stroke prevention education package to improve the lifestyle behavioral factors could reduce the vascular events. The Secondary Prevention by Structured Semi-Interactive Stroke Prevention Package (SPRINT) study in India aims to assess the role of a stroke prevention education package to reduce recurrent strokes, myocardial infarction, and death in patients with stroke. The objective is to formulate the detailed statistical analysis plan for the SPRINT India prior to trial unblinding. Methods: The plan was developed by trial statisticians with the help of principal investigator and management team of the SPRINT study. The chosen primary and secondary outcome measures and knowledge of critical baseline data were used to construct the statistical analysis plan. All collected data will be thoroughly reviewed. Patient baseline characteristics will be summarized with relevant descriptive statistics. The findings are planned and explained for the most appropriate statistical comparison between the groups. Results: The final statistical analysis plan corresponds to established criteria and will allow for transparent and efficient reporting. Conclusions: The SPRINT trial statistical analysis plan is developed in order to avoid analysis bias arising from prior knowledge of findings and to explicitly summarize prespecified analyses.
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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 | 0.109 | 0.137 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".