Predictors and Functional Outcomes Associated With Longitudinal Trajectories of Anxiety and Depression from 2 to ≥36 Months After Moderate to Severe Traumatic Brain Injury
Bibliographic record
Abstract
This study investigated longitudinal trajectories of anxiety and depressive symptoms following moderate-severe traumatic brain injury (TBI), predictors of the trajectories, and associations with 1-year return to productivity. One hundred forty-eight patients with moderate-severe TBI were assessed at 2, 5, 12, and ≥36 months post-injury on the Beck Anxiety Inventory and the Beck Depression Inventory. Clinical interviews obtained information about demographics, injury characteristics, and 1-year return to productivity. Latent growth mixture modeling identified trajectories of anxiety and depression across time. The three-step method identified predictors of trajectories, and χ 2 analyses determined associations between trajectories and 1-year return to productivity. Analyses revealed that four-class models of anxiety and depression best fit the data. Most individuals had stable minimal (67%) or low (18%) levels of anxiety over time. Two other subsets of individuals were classified by anxiety that worsened rapidly (7%) or improved in the 1st year but worsened by 3 years post-injury (9%). Similarly for the depression trajectories, most individuals had stable minimal (70%) or low (10%) levels of depression over time. Others had depression that worsened rapidly (12%) or was delayed, with onset 1-year post-injury (8%). Predictors of worsening anxiety and depression included younger age, less education, and male gender. Those with worsening anxiety or depression were less likely to return to productivity by 1-year post-injury. There is a significant burden of anxiety (15%) and depression (20%) in the 3 years after moderate-severe TBI. Future research targeting at-risk patients may help to improve quality of life and functional recovery.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".