411 Acute concussion versus post-concussion syndrome (PCS): how can we prevent progression?
Bibliographic record
Abstract
Background Concussions can be classified as acute (<90 days to resolution) or post-concussion syndrome (PCS, ≥90 days to resolution). PCS poses a great burden to the individual and to public health. Objective To contrast the presentation and recovery of acute concussion and PCS to identify potential factors for PCS prevention. Design Retrospective chart review of concussion patients seen by Sports and Exercise Medicine physicians from 2015–2019. Setting Glen Sather Sports Medicine Clinic, Edmonton, Alberta, Canada. Patients 496 patients (289 male/207 female, 19.7±9.4 years) presented with 561 concussions in 1471 visits. Assessment of Risk Factors Concussions were subdivided into acute and PCS by time from injury to first appointment. Main Outcome Measurements Demographics, injury mechanisms, Standardized Concussion Assessment Tool (SCAT) scores, management, and recovery timelines. Results Acute concussions accounted for 88% of injuries and 12% were PCS. Females (RR=1.4) and adults ≥ 25 years (RR=3.6) were more likely to be diagnosed with PCS. In both, injuries occurred most commonly in hockey, football, and soccer. Family physicians were the most frequent referral provider (58% acute, 76% PCS). Median injury-appointment time was 11.0 days (acute) compared to 182.0 days (PCS). Initial total SCAT symptom score was significantly greater (p<0.001) in PCS (56.0±33.0) compared to acute concussion (39.8±31.9). Therapies (i.e. referral, medication, intervention) were prescribed in 44% of acute injury visits compared to 73% of PCS visits (χ2=88.6, p<0.00001). Recovery timelines for return to work, school, and sport were significantly longer in PCS patients than in those with acute concussions (p<0.05). Conclusions Athletes who are female and/or ≥25 years of age may be at greater risk for PCS progression, requiring closer monitoring and further injury prevention efforts. Considering the number of referrals from family physicians, further concussion education may better optimize initial management and shorten delays in seeking necessary sports medicine consultation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".