Depression in the First Year after Traumatic Brain Injury
Bibliographic record
Abstract
The aims of this study were to document the frequency of major and minor depressive episodes in the first year after traumatic brain injury (TBI), taking into account TBI severity and pre-morbid history of major depression, and to describe trajectories of depressive episodes. Participants were 227 adults who were hospitalized post-TBI (76% male; mean age = 41 years; 50% mild, 33% moderate, and 17% severe TBI). Major and minor depressive episodes were assessed with the Mini International Neuropsychiatric Interview at three time points (4, 8, and 12 months after TBI). Overall, 29% of participants had a major depressive episode in at least one of the three assessments, with fairly stable rates across assessments. Participants with mild TBI were more likely than those with moderate/severe TBI to be diagnosed with major depression, as were individuals with a positive pre-morbid history of depression compared to those without such history. In addition, 13% of participants had a minor depressive episode in at least one of the three assessments. Rates of minor depression significantly decreased from 4 to 8-12 months post-injury. Results also revealed a wide variety of trajectories of depressive episodes across assessments. Of note, 52% of major depression cases still fulfilled diagnostic criteria 4 months later, whereas 38% of minor depression cases deteriorated to major depression at the following assessment. These findings suggest that depression is highly prevalent after TBI, and monitoring of patients with subthreshold depressive symptoms is warranted in order to prevent the development of full-blown major depressive episodes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".