The Postconcussion Syndrome in Sports and Recreation
Bibliographic record
Abstract
BACKGROUND: There are still many unanswered questions about postconcussion syndrome (PCS) in sports and recreation. The predictors of PCS are unknown, although a history of previous concussion has been suspected. OBJECTIVE: To explore the clinical features and demography of PCS in athletes. METHODS: A retrospective cohort study was performed by chart review of clinical and demographic data of 285 consecutive concussed patients, 138 of whom had sports-related PCS based on ≥ 3 postconcussion symptoms lasting ≥ 1 month. RESULTS: The 138 athletes with PCS averaged 22.8 years of age, and 70 (50.7%) were ≤ 18 years of age. They averaged 3.4 concussions (range, 1 to > 12). Only 19.6% had no previous concussion. There was a history of previous psychiatric condition, attention-deficit disorder or attention-deficit/hyperactive disorder, learning disability, or previous migraine headaches in 21.0%. Ice hockey caused the highest number of the most recent concussions at 72 cases (52.2%). Soccer, snow skiing, equestrian sports, and basketball were less frequent causes. The average number of persistent symptoms was 7.6, and the median duration of PCS was 6 months at the first examination. CONCLUSION: More than 80% of PCS cases had at least 1 other previous concussion. Half of the athletes with PCS were ≤ 18 years of age. PCS was associated with 7.6 symptoms per athlete. The duration of PCS and the number of symptoms were not related to the number of previous concussions, loss of consciousness, or return to play. Further research on treatment and prevention of PCS is required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".