The status of telerehabilitation in neurological applications
Bibliographic record
Abstract
We systematically reviewed the evidence for the effectiveness of tele-neurorehabilitation (TNR) applications. The review included recent reports on rehabilitation for any disability associated with a neurological deficit or condition. Study quality was assessed using an approach that considered both study performance and study design. Judgements were made on whether each application had been successful, and whether further data were needed to establish the application as suitable for routine use. Nineteen credible studies that reported patient outcomes or administrative changes were identified. These studies related to 13 conditions. The focus of rehabilitation included Internet-supported treatments for management of fatigue, pain and depression; promotion of physical activity; and speech therapy. Sixteen studies were of high or good quality and three were fair to good, with some limitations. In 13 of the 19 studies the TNR application was successful in providing at least equivalent outcomes to conventional approaches. Additional work would be needed on eight applications to establish suitability for routine use, and would be desirable in five. Thus the recent literature provides further support for TNR applications, showing the promise of this field in a number of areas. However, the database of credible studies remains small.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".