Research priorities for diagnosis, prognosis, and rehabilitation following concussion: results from a national survey of Australian health professionals
Bibliographic record
Abstract
PURPOSE: Recently, the Concussion James Lind Alliance Priority Setting Partnership (JLAPSP) (Canada) identified serious research gaps regarding diagnosis, management, and access to effective rehabilitation for concussion/mild traumatic brain injury (mTBI). Our aim was to determine if the same research priorities are important to Australian health professionals working in the concussion/mTBI field. MATERIALS AND METHODS: professional networks, social media, professional group listservs, a research project noticeboard, and at conferences. It comprised of 25 of the highest ranked concussion research questions from the JLAPSP. We examined how professionals ranked the research questions and analyzed variation in ranking by clinical role and concussion/mTBI work experience. RESULTS: Our sample of 187 participants included medical and allied health professionals. Most participants were occupational therapists (22%), physiotherapists (18%), neuropsychologists (17%), and worked in Victoria (47%), New South Whales (18%), or Queensland (15%) in metropolitan areas. Health professionals ranked three research questions highest: identifying methods to predict prolonged recovery; effectiveness of early referral and treatment by a specialized concussion/mTBI team; and implementation studies on upskilling healthcare workers. CONCLUSIONS: The research priorities identified can guide research efforts to improve the assessment, management, and rehabilitation of individuals with concussion/mTBI in Australia.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".