Characterizing the profiles of patients with acute concussion versus prolonged post-concussion symptoms in Ontario
Bibliographic record
Abstract
Identifying vulnerability factors for developing persisting concussion symptoms is imperative for determining which patients may require specialized treatment. Using cross-sectional questionnaire data from an Ontario-wide observational concussion study, we compared patients with acute concussion (≤ 14 days) and prolonged post-concussion symptoms (PPCS) (≥ 90 days) on four factors of interest: sex, history of mental health disorders, history of headaches/migraines, and past concussions. Differences in profile between the two groups were also explored. 110 patients with acute concussion and 96 patients with PPCS were included in our study. The groups did not differ on the four factors of interest. Interestingly, both groups had greater proportions of females (acute concussion: 61.1% F; PPCS: 66.3% F). Patient profiles, however, differed wherein patients with PPCS were significantly older, more symptomatic, more likely to have been injured in a transportation-related incident, and more likely to live outside a Metropolitan city. These novel risk factors for persisting concussion symptoms require replication and highlight the need to re-evaluate previously identified risk factors as more and more concussions occur in non-athletes and different risk factors may be at play.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".