A-14 Clinical Outcome Following Concussion Among College Athletes with a Prior Concussion History: A Systematic Review
Bibliographic record
Abstract
Abstract Purpose: This systematic review examined the association between prior concussion history and clinical outcomes following concussion among collegiate athletes. Methods: This review was registered with PROSPERO database for systematic reviews (protocol ID: CRD42016041479 & CRD42019128300) and adhered to PRISMA guidelines. Three searches of nine online databases were conducted: (1) database inception to June of 2016; (2) January 1, 2016 to February 1, 2019; and (3) February 1, 2019 to May 15, 2021. We screened 5118 abstracts and 619 full-text articles were reviewed. We utilized a likelihood heuristic to assess evidence for an association between concussion history and clinical outcomes. Results: Sixteen studies met inclusion criteria, and 13 studies reported the number of participants with a history of prior concussions (≥1)—which totaled 1690 of 4573 total participants (37.0%). Newcastle-Ottawa risk of bias ratings ranged from 3 to 9 (mean = 5.4, SD = 1.4). Across all studies, 43.8% (k = 7/16) reported a statistically significant result among primary analyses showing an association between concussion history and worse clinical outcome. A minority of studies reporting on symptom duration (4/13, 30.8%) and time to return to play (2/7, 28.6%) found an association between concussion history and worse outcome. Conclusions: The question of whether college athletes with a history of concussion have, on average, worse clinical outcome from their next concussion remains unresolved. Many studies to date are small, and only three focused specifically on this topic. Important clinical outcomes, such as time to return to academics, have not been adequately studied. Larger hypothesis-driven studies are needed.
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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 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".