Development of the Concussion Recovery Questionnaire: a self-report outcome measure of functional status following concussion (PhD Academy Award)
Bibliographic record
Abstract
I employed an iterative, patient-centred approach to develop a concussion-specific functional status measure. While most adults fully recover from concussion, at least 15% exhibit ongoing symptoms including headache, fatigue and difficulty concentrating for years resulting in high degrees of disability.1 Since no objective biomarker exists for concussion, self-reported symptom burden is the most commonly reported recovery measure, along with cognitive function, balance and exercise tolerance in terms of frequency, duration, difficulty or agreement level. Clinical tests of cognition and balance often appear normal beyond the acute phase. Symptoms are non-specific and commonly present in other conditions such as depression, anxiety and post-traumatic stress disorder.1 Persons with concussion often manage their symptoms by limiting provocative activities and social participation. A low symptom score may thus reflect reduced life activities rather than functional recovery. Prior to my research, no concussion-specific measure of functional status existed. I conducted this research to address a gap in clinical tools within …
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".