Prognosticators of Persistent Symptoms Following Pediatric Concussion
Bibliographic record
Abstract
OBJECTIVE To identify predictors of persistent concussion symptoms (PCS) in children following concussion. DATA SOURCES We searched MEDLINE, Embase, and the Cochrane Library to April 2012. STUDY SELECTION A systematic review of the literature to identify prognosticators of PCS following pediatric concussion was conducted. Studies evaluating patients aged 2 years to 18 years with PCS were eligible. MAIN OUTCOME MEASURES The association of clinically available factors with PCS development. RESULTS A literature search yielded 824 records; 561 remained after removal of duplicates. Fifteen studies were included in descriptive analysis; heterogeneity precluded a meta-analysis. Larger prospective studies concluded that the risk for PCS was increased in older children with loss of consciousness, headache, and/or nausea/vomiting. Smaller studies noted that initial dizziness may predict PCS. Patients with premorbid conditions (eg, previous head injury, learning difficulties, or behavioral problems) may also have increased risk. CONCLUSIONS Minimal, and at times contradictory, evidence exists to associate clinically available factors with eventual development of PCS in children. Future trials must be adequately powered to determine which variables best predict the time to full symptom resolution. Expert consensus should delineate which postconcussion assessment measures are preferred to reduce heterogeneity going forward. Research to improve care for the epidemic of pediatric concussion depends on early identification of those most in need of intervention.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".