Suicidal ideation after mild traumatic brain injury: a consecutive canadian sample
Bibliographic record
Abstract
This study aims to elucidate psychosocial and injury features contributing to SI following concussion or mild traumatic brain injury (mTBI) and the time course for its development. Between 1998 and 2012, a sample of 871 patients referred to a follow-up clinic after concussion treatment in an urban tertiary care ED were consecutively offered enrollment at 3 months post injury. Data from psychiatric and social-demographic assessments were consecutively collected at 2 visits (3 and 6 months after injury) respectively. Chi-square and t-tests were performed to identify associations between variables related with SI. Logistic regression analysis was performed to identify factors independently associated. During the enrolment period, 2,296 patients with mTBI presented to the ED. 871 adults completed psychiatric and social demographic clinic assessments at 3 months, and 500 returned at 6 months. Suicidal ideation was expressed by 6.3% at 3 months and 8.2% at 6 months. Regression models showed SI independently associated with: speaking English as a second language (ESL) and injury mechanism (MVC passenger) at 3 and 6 months; and history of depression and marital status at 3 months only. SI is common 3 months after mTBI, and appears more at 6 month follow up. These findings suggest earlier screening for predisposing factors and closer monitoring of those at risk for suicidality.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".