The Post-Concussion Symptom Scale: Normative Data for Adolescent Student-Athletes Stratified by Gender and Preexisting Conditions
Bibliographic record
Abstract
BACKGROUND: The Post-Concussion Symptom Scale (PCSS) is a self-report questionnaire measuring symptoms that commonly occur after a concussion; however, these symptoms are nonspecific and can be related to co-occurring orthopaedic injuries (eg, cervical strain) or patient characteristics and preexisting conditions, even in the absence of a recent injury. As such, clinicians may have difficulty determining whether symptom elevations are attributable to a recent concussion as opposed to a confounding injury or a preexisting condition, which may be especially difficult when preinjury baseline symptom data are unavailable. PURPOSE: This study aimed to further validate the 4-factor model of the PCSS (ie, cognitive, sleep-arousal, physical, and affective symptoms) with adolescent student-athletes and provide normative reference data for each factor and the total score, stratified by gender and preexisting health conditions. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Participants were 9358 adolescent student-athletes who completed the PCSS during a preseason baseline evaluation (mean age, 14.9 years; SD, 1.3 years [range, 13-18 years]; 49.3% boys). The 4-factor model of the PCSS was tested for the full sample and separately for boys and girls using confirmatory factor analysis. Symptom severity percentiles were created for the PCSS total score and each factor, stratified by gender and preexisting conditions (ie, attention-deficit/hyperactivity disorder, mental health history, headache/migraine history, learning disability/dyslexia, academic problems, and concussion history). RESULTS: The 4-factor model of the PCSS replicated in the full sample (comparative fit index [CFI] = 0.959) and in both gender groups (boys: CFI = 0.961; girls: CFI = 0.960). The total PCSS score at the 84th percentile varied by preexisting conditions as follows: healthy participants = 8, attention-deficit/hyperactivity disorder = 18, mental health history = 26, headache/migraine history = 18, learning disability = 19, and academic problems = 17. On all PCSS subscales, participants with a mental health history had the highest scores, and high scores were associated with having >1 preexisting condition. Girls had higher scores than boys for each stratification. CONCLUSION: The 4-factor model of the PCSS replicates for adolescent student-athletes. Gender, number of preexisting conditions, and mental health history are important factors to account for when interpreting PCSS symptom severity. The normative data provided herein could assist clinicians in determining whether an adolescent student-athlete is presenting with persistent postconcussion symptoms or a typical symptom experience based on their gender and personal health history.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".