Postconcussion syndrome: demography and predictors in 221 patients
Bibliographic record
Abstract
Background: Most individuals recover from a concussion within 7-10 days. However recovery may be very prolonged. Individuals who do not recover within the usual time are said to have postconcussion syndrome (PCS). The objective of this study was to examine the demography and predictors of PCS. Methods: This was a retrospective cohort study of 284 consecutive concussed patients 221 of whom had PCS on the basis of at least three symptoms persisting at least 1 month. A uniform, internationally accepted definition of concussion was used. Results: The 221 cases showed considerable heterogeneity in clinical features of PCS. They averaged 3.3 concussions with a range of 0 to 12+ concussions, and 62.4% occurred during sports and recreation. The median duration of PCS was 7 months at the time of examination, with 11.8% lasting more than 2 years. Surprisingly, 23.1% with PCS had only 1 concussion. The average age was 27 years (range 10-74). The average number of persistent symptoms was 8.1. 26.2% had a previous psychiatric condition, ADD/ADHD, a learning disability, or previous migraine headaches. The prevalence of arachnoid cysts and Chiari malformation in PCS exceeded the general population. Conclusions: In most of our cases, PCS was disabling, and lasted for months or years.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".