Alexithymia After Traumatic Brain Injury: Its Relation to Magnetic Resonance Imaging Findings and Psychiatric Disorders
Bibliographic record
Abstract
OBJECTIVE: People with traumatic brain injury (TBI) were studied to assess the prevalence of alexithymia and its relationship to magnetic resonance imaging (MRI) findings and psychiatric disorders. METHODS: Fifty-four participants, 67% men, were evaluated after a median of 30 years since TBI. A control group was matched for age, gender, and severity of depression. Alexithymia was measured with the 20-item Toronto Alexithymia Scale (TAS-20). In patients with TBI, axis I psychiatric disorders were assessed with the Schedules for Clinical Assessment in Neuropsychiatry (SCAN, version 2.1), and axis II disorders with the Structured Clinical Interview for DSM-III-R Personality Disorders (SCID-II). MRI examinations were carried out with a 1.5 T MRI scanner. RESULTS: Alexithymia was significantly more common in patients with TBI than in controls (31.5% versus 14.8%; odds ratio 2.64, 95% confidence interval 1.03-6.80). None of the variables representing TBI, ie, severity of TBI or the presence, laterality, or location of contusions on MRI, was associated with the TAS-20 total scores. Several current axis I and II psychiatric disorders, particularly organic personality syndrome, were connected to higher TAS-20 scores. CONCLUSION: Alexithymia is common, along with psychiatric disorders, in patients with TBI. Both of them may reflect dysfunction of the injured brain. In clinical practice, alexithymic features should be taken into consideration in psychosocial rehabilitation 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".