Interdisciplinary intervention (GAIN) for adults with post-concussion symptoms: a study protocol for a stepped-wedge cluster randomised trial
Bibliographic record
Abstract
BACKGROUND: Persistent post-concussion symptoms (PCS) are associated with prolonged disability, reduced health-related quality of life and reduced workability. At present, no strong evidence for treatments for people with persistent PCS exists. Our research group developed a novel intervention, "Get going After concussIoN (GAIN)", that incorporates multiple evidence-based strategies including prescribed exercise, cognitive behavioural therapy, and gradual return to activity advice. In a previous randomised trial, GAIN provided in a hospital setting was effective in reducing symptoms in 15-30-year-olds with PCS 2-6 months post-injury. In the current study, we describe the protocol for a trial designed to test the effectiveness of GAIN in a larger municipality setting. Additionally, we test the intervention within a broader age group and evaluate a broader range of outcomes. The primary hypothesis is that participants allocated to enhanced usual care plus GAIN report a higher reduction in PCS 3 months post-intervention compared to participants allocated to enhanced usual care only. METHODS: The study is a stepped-wedge cluster-randomised trial with five clusters. The 8-week interdisciplinary GAIN program will be rolled out to clusters in 3-month intervals. Power calculation yield at least 180 participants to be enrolled. Primary outcome is mean change in PCS measured by the Rivermead Post-Concussion Symptoms Questionnaire from enrolment to 3 months after end of treatment. Secondary outcomes include participation in and satisfaction with everyday activities, labour market attachment and other behavioural measures. Self-reported outcomes are measured at baseline, by end of treatment and at 3, 6, and 18 months after end of treatment. Registry-based outcomes are measured up to 36 months after concussion. DISCUSSION: The trial will provide important information concerning the effectiveness of the GAIN intervention in a municipality setting. Furthermore, it will provide knowledge of possible barriers and facilitators that may be relevant for future implementation of GAIN in different settings. TRIAL REGISTRATION: The current GAIN trial is registered in ClinicalTrials.gov (study identifier: NCT04798885 ) on 20 October 2020.
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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 | 0.036 | 0.034 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.012 |
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".