Effectiveness of personalized rehabilitation in adults suffering from persistent concussion symptoms as compared to usual care: a randomized control trial protocol
Bibliographic record
Abstract
Abstract Background Symptoms reported by patients who sustain a concussion are non-specific. As such, clinicians are better able to manage patients when a standardized clinical exam is performed to sub-type the driver(s) of symptoms. Aerobic exercise and multimodal rehabilitation have consistently shown to be a possibly effective means to manage this population; however, the optimal training prescription is unclear. Thus, there is a need to further examine the effectiveness of personalized rehabilitative treatments. Our primary aim is to evaluate the response to personalized therapy on recovery, as measured by The Rivermead Post-concussion Symptoms Questionnaire (RPQ) when compared to an active control. Methods We will conduct a multi-center 12-week case-crossover randomized controlled trial. 50 participants will be recruited from out-patient University Health Network clinics and community-based clinical practices around the greater Toronto area. Participants will be randomized at baseline to Group A: a personalized care program followed by an active control or Group B: an active control followed by a personalized care program. Participants will be included should they be 21 years of age and older and have symptoms that have persisted beyond 4 weeks but less than 1 year. Participants will undergo 6-weeks of care in their respective streams. After 6-weeks, participants will undergo a re-examination. They will then crossover and undertake the alternative treatment for 6 weeks. At the end of 12 weeks, participants will undertake the endpoint examinations. The primary outcome will be the Rivermead Postconcussion Questionnaire (RPQ). The secondary outcomes will be changes in standardized clinical examination, Neck Disability Index (NDI), Patient Health Questionnaire (PHQ-9) and an electroencephalography (EEG) via NeuroCatch ™ . The statistical analysis to be performed is composed of an adjusted model using an analysis of variance, specifically using an unpaired t-test to test for associations between variables and outcomes. Discussion Given the recommendations from reviews on the topic of rehabilitation for adults with persistent concussion symptoms, we are undertaking a controlled trial. The documented high costs for patients seeking care for persistent symptoms necessitate the need to evaluate the effectiveness of a personalized rehabilitative program compared to the current standard of care. Trial registration ClinicalTrials.gov ID: NCT06069700.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".