Statistical Analysis Plan (SAP) for AyuRvedic TrEatment in the Rehabilitation of Ischemic STrOke Patients in India: A Randomized Controlled Trial (RESTORE)
Bibliographic record
Abstract
Background Stroke often results in a loss of functional ability. The evaluation of recovery after a stroke is crucial for both treatment and research. The AyuRvedic TrEatment in the Rehabilitation of Ischemic STrOke Patients in India: A randomized controlled trial (RESTORE) aims to generate evidence-based data for creating a uniform rehabilitation protocol and initiating a cross-referral practice to support an integrative treatment approach. The objective is to develop a detailed statistical analysis plan for the RESTORE trial prior to data analysis. Methods The statistical analysis plan was developed by the trial statistician with the assistance of the RESTORE trial principal investigator and the trial management team. The statistical analysis plan was built using the specified primary and secondary outcome measures, as well as knowledge of important baseline data. All data collected will be thoroughly examined. Results The final statistical analysis plan corresponds to established criteria and will allow for transparent and efficient reporting. Conclusions The RESTORE trial statistical analysis plan is created to reduce analysis bias caused by prior knowledge of results and to explicitly outline prespecified analysis.
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 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 | 0.088 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".