Inhibition and object relations in borderline personality traits after traumatic brain injury
Bibliographic record
Abstract
This study aims to assess the nature and severity of borderline traits after traumatic brain injury (TBI). Thirty subjects with moderate or severe TBI were compared to 30 normal controls on the Revised Diagnostic Interview for Borderlines (DIB-R), a dimensional measure of borderline traits, the Go-no go inhibition task, the Complexity of Representations of People and Affect-Tone Relationships Paradigms, two scales from the Social Cognition and Object Relations Scale (SCORS) evaluating the quality of object relations, an estimation of pre-morbid borderline severity, the Beck Depression Inventory (BDI) and various neuropsychological measures. Results indicate that TBIs present more borderline symptoms and traits than controls. However, the severity of borderline symptomatology remains comparatively low for the vast majority. As expected, the TBI group showed a poorer performance on the Go-no go task, a characteristic neuropsychological inhibition deficit. Yet, both samples present similar profiles on the SCORS. Finally, the DIB-R was correlated with the Affect-Tone scale, the BDI and with the pre-morbid severity estimation. Results suggest that post-TBI borderline traits remain rare and relate more to the affective quality of object relations, negative affects and pre-morbid borderline pathology than inhibition deficits.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".