Rates and Predictors of Suicidal Ideation During the First Year After Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVES: We examined rates of suicidal ideation (SI) after traumatic brain injury (TBI) and investigated whether demographic characteristics, preinjury psychiatric history, or injury-related factors predicted SI during the first year after injury. METHODS: We followed a cohort of 559 adult patients who were admitted to Harborview Medical Center in Seattle, Washington, with a complicated mild to severe TBI between June 2001 and March 2005. Participants completed structured telephone interviews during months 1 through 6, 8, 10, and 12 after injury. We assessed SI using item 9 of the Patient Health Questionnaire (PHQ-9). RESULTS: Twenty-five percent of the sample reported SI during 1 or more assessment points. The strongest predictor of SI was the first PHQ-8 score (i.e., PHQ-9 with item 9 excluded) after injury. Other significant multivariate predictors included a history of a prior suicide attempt, a history of bipolar disorder, and having less than a high school education. CONCLUSIONS: Rates of SI among individuals who have sustained a TBI exceed those found among the general population. Increased knowledge of risk factors for SI may assist health care providers in identifying patients who may be vulnerable to SI after TBI.
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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 | 0.004 |
| 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".