Prognostic models for depression and post-traumatic stress disorder symptoms following traumatic brain injury: a CENTER-TBI study
Bibliographic record
Abstract
Background Traumatic brain injury (TBI) is associated with an increased risk of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). We aimed to identify predictors and develop models for the prediction of depression and PTSD symptoms at 6 months post-TBI. Methods We analysed data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury study. We used linear regression to model the relationship between predictors and depression (Patient Health Questionnaire-9) and PTSD symptoms (PTSD Checklist for Diagnostic and Statistical Manual for Mental Health Disorders Fifth Edition). Predictors were selected based on Akaike’s Information Criterion. Additionally, we fitted logistic models for the endpoints ‘probable MDD’ and ‘probable PTSD’. We also examined the incremental prognostic value of 2–3 weeks of symptoms. Results We included 2163 adults (76% Glasgow Coma Scale=13–15). Depending on the scoring criteria, 7–18% screened positive for probable MDD and about 10% for probable PTSD. For both outcomes, the selected models included psychiatric history, employment status, sex, injury cause, alcohol intoxication and total injury severity; and for depression symptoms also preinjury health and education. The performance of the models was modest (proportion of explained variance=R 2 8% and 7% for depression and PTSD, respectively). Symptoms assessed at 2–3 weeks had a large incremental prognostic value (delta R 2 =0.25, 95% CI 0.24 to 0.26 for depression symptoms; delta R 2 =0.30, 95% CI 0.29 to 0.31 for PTSD). Conclusion Preinjury characteristics, such as psychiatric history and unemployment, and injury characteristics, such as violent injury cause, can increase the risk of mental health problems after TBI. The identification of patients at risk should be guided by early screening of mental health.
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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 | 0.028 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".