Role of alexithymia in suicide ideation after traumatic brain injury
Bibliographic record
Abstract
A high frequency of suicide ideation (SI) has been reported following traumatic brain injury (TBI) (Simpson & Tate, 2002; Teasdale & Engberg, 2001). This study examined the frequency of SI following TBI, and its relationship to alexithymia, and depression, plus two components of depression-hopelessness and worthlessness. One hundred and five TBI patients and 74 demographically matched controls completed the Toronto Alexithymia Scale-20 (TAS-20) and the Beck Depression Inventory (BDI-II). Ratings of SI, hopelessness, and worthlessness were extracted from the BDI-II. Results confirm a high frequency of SI (33%) and alexithymia (61%) after TBI compared with healthy controls (1.4% and 6.5%, respectively). A high frequency of alexithymia was also found in a sub-group of moderate-severely depressed TBI patients (70.68%) compared with two non-TBI depressed samples (53.92% and 44.8%). A significant association was found between SI and alexithymia in the TBI group, with the SI group reporting significantly higher TAS-20 total scores. However, logistic regression analysis found that worthlessness was the strongest predictor of SI after TBI. The results of this study suggest that increased attention should be directed toward emotional change after TBI, as alexithymia may mediate the development of worthlessness and, in turn, increase the risk of SI.
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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 | 0.003 |
| 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".