Post-concussive depression: evaluating depressive symptoms following concussion in adolescents and its effects on executive function
Bibliographic record
Abstract
Background: Post-concussive depression describes an elevation of depressive symptoms following concussion that occurs in conjunction with other symptoms of concussion. Children with concussion are more likely to diagnosed with depression. The overlapping symptoms between clinical depression and concussion make the diagnosis of depression difficult. The purpose of this study is to explore how post-concussive depression relates to post-concussion symptoms and cognition by investigating symptom-reporting in youth with post-concussive depression and executive function.Methods: Adolescents (age 10–17 years) diagnosed with concussion were divided into two groups based on depression scores on the Children’s Depression Inventory (post-concussion depression; non-depression groups). Symptom reporting on the Post-Concussion Symptom Inventory and performance on Immediate Post-concussion Assessment and Cognitive Testing (ImPACT) were compared.Results: Participants with post-concussive depression had heightened emotionality, irritability, and nervousness. Sadness and fatigue were reported by both groups. ImPACT was unable to distinguish between groups but the group overall demonstrated severe neurocognitive deficits.Conclusion: Reports of greater emotionality, irritability, and nervousness on concussion symptom scales may be indicators of post-concussion depression. It is important for clinicians to take note when an adolescent with concussion scores high on these three emotional symptoms as they may be indicative of greater emotional distress.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".