The Necessity and Limitations of Evidence-Based Practice in Stroke Rehabilitation
Bibliographic record
Abstract
Evidence-based practice attempts to implement the results of research trials (evidence) and to translate those results into clinical practice, with the presumed goal of improving the effectiveness of clinical care. An empirically validated knowledge base has the potential to substantially increase a clinician's confidence in his or her therapeutic plan. Whether or not research can be a basis for change in practice is a serious ethical consideration. This article argues for the importance of transferring research knowledge on the effectiveness of stroke rehabilitation as quickly as possible to clinical practice, while at the same time acknowledging the ethical issues associated with this process.
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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 | 0.099 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".