Bibliographic record
Abstract
Borderline Personality Disorder (BPD) is characterized by Metacognition/Mentalization deficits and Emotion Dysregulation (ED). BPD’s first-choice treatment is psychotherapy, but a comprehensive model has not yet been formulated, consequently also treatments result controversial. \n \nStudy 1: \nAims: to examine the relationships between Metacognitive Functions and ED and other clinical features in a BPD sample. \nMethods: Seventy patients were assessed for the inclusion and 45 BPD patients were included. Metacognitive functions were evaluated with Metacognition Assessment Interview (MAI). Specific self-reports measured respectively: ED (Difficulties in Emotion Regulation Scale, DERS), Alexithymia (Toronto Alexithymia Scale, TAS), Impulsiveness (Barratt Impulsiveness Scale, BIS), Mood (Beck Depression Inventory; BDI), Interpersonal Sensitivity (Inventory of Interpersonal Problems, IIP) and general psychopathology (Global Severity Index (GSI) of SCL-90). \nA Structural Equation Model (SEM) was used to evaluate the relations between variables. \nResults: SEM showed that TAS score resulted a mediator between MAI total score and DERS score and DERS significantly predicted BIS, BDI, IIP and GSI scores. \nConclusions: The general level of psychopathology and the other clinical variables seemed directly linked to ED. ED didn’t seem to correlate directly to Metacognition, but indirectly through Alexithymia. \n \nStudy 2: \nAims: to compare the effect of 1-year Metacognitive Interpersonal Therapy (MIT) and Mentalization Based Therapy (MBT) vs TAU (Treatment as usual) on Metacognition functions, ED and other clinical features in a BPD sample. \nMethods: Forty-five patients were divided in 3 groups: MIT (N=14), MBT (N=16) and TAU (N=15). MAI scores were the primary outcome, DERS, TAS, BIS, BDI, IIP and GSI of SCL-90 scores were the secondary outcomes. \nLinear Mixed model were used for the longitudinal evaluation of the results. \nResults: MAI total score improve in both experimental groups. Secondary outcomes improved, but the effect wasn’t statistically significant. \nConclusions: Differentiation and Integration played a central role both in MIT and MBT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".