Integrating artificial intelligence in physical therapy: a quasi-experimental study comparing conventional treatment with a hybrid intervention using telerehabilitation for workers with whiplash syndrome
Bibliographic record
Abstract
INTRODUCTION: AI-assisted telerehabilitation enables remote assessment and monitoring of patient movement, improving access to treatment while reducing costs. This study aimed to compare conventional center-based physical therapy with a hybrid intervention that combines face-to-face physiotherapy and AI-supervised telerehabilitation in workers affected by cervical whiplash syndrome. METHODS: The study population comprised workers aged 16 to 65 years requiring physical therapy for cervical whiplash syndrome (ICD-10 S13.4XXA). The primary outcome was the number of face-to-face sessions, while sickness absence duration was also analyzed. The sample included 15 patients in the telerehabilitation group and 28 in the conventional treatment group. The intervention included AI-supervised therapeutic exercises on days when patients received treatment at home, whereas on days they attended the rehabilitation center, they received the same standard treatment as the control group. RESULTS: The median number of face-to-face sessions was significantly lower in the telerehabilitation group (6 vs. 9), with differences remaining significant after adjustment. The duration of sickness absence was not longer in the telerehabilitation group (18 vs. 21 days). CONCLUSION: Reducing the number of face-to-face sessions through AI-assisted telerehabilitation optimizes physiotherapy resource utilization without prolonging sickness absence, thereby enhancing healthcare efficiency.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".