2.13 Higher fear avoidance in athletes correlates to more acute concussive symptoms and severity
Bibliographic record
Abstract
Objective The purpose of our study was to assess the relationship between fear avoidance and acute concussion symptoms in athletes. Design A cross-sectional study. Setting On the respective college campuses. Participants We had 34 participants, with an average age of 20.9 ± 1.8 years old, including 23 males and 11 females. Interventions (or Assessment of Risk Factors) We assessed the athletes’ concussions within 48 hrs using the SCAT5, pain catastrophizing using the Pain Catastrophizing Scale, fear avoidance using the Athlete Fear Avoidance Questionnaire (AFAQ), and anxiety and depression using the Hospital Anxiety and Depression Questionnaire (HADS). Outcome Measures The dependent variables were AFAQ score, HADS score and PCS score. Main Results Our participants suffered an average of 7.4 ± 5.1 symptoms and a 16.3 ± 17.0 symptom severity score. The total number of symptoms was significantly associated with the AFAQ score (r=0.493). Moreover, the symptom severity score was associated with the AFAQ score (r=0.481). The total number of symptoms and severity score were significantly associated with the HADS score (r=0.686 and r=0.602). The AFAQ score, HADS depression and HADS anxiety scores model was a significant predictor of the total number of symptoms reported on the SCAT5, accounting for 50.4% of the variance (p>0.001) as well as severity (p=0.001). Conclusions Our study identified a significant relationship between athlete fear avoidance, depression, and anxiety and the number of acute concussion symptoms in athletes. A higher fear avoidance means that patients report more symptoms, and this relationship could explain why there is variability in the reporting of concussion symptoms.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".