Specificity of affective instability in patients with borderline personality disorder compared to posttraumatic stress disorder, bulimia nervosa, and healthy controls.
Bibliographic record
Abstract
Affective instability is a core feature of borderline personality disorder (BPD). The use of advanced assessment methodologies and appropriate statistical analyses has led to consistent findings that indicate a heightened instability in patients with BPD compared with healthy controls. However, few studies have investigated the specificity of affective instability among patients with BPD with regard to relevant clinical control groups. In this study, 43 patients with BPD, 28 patients with posttraumatic stress disorder (PTSD), 20 patients with bulimia nervosa (BN), and 28 healthy controls carried e-diaries for 24 hours and were prompted to rate their momentary affective states approximately every 15 minutes while awake. To quantify instability, we used 3 state-of-the-art indices: multilevel models for squared successive differences (SSDs), multilevel models for probability of acute changes (PACs), and aggregated point-by-point changes (APPCs). Patients with BPD displayed heightened affective instability for emotional valence and distress compared with healthy controls, regardless of the specific instability indices. These results directly replicate earlier studies. However, affective instability did not seem to be specific to patients with BPD. With regard to SSDs, PACs, and APPCs, patients with PTSD or BN showed a similar heightened instability of affect (emotional valence and distress) to that of patients with BPD. Our results give raise to the discussion if affective instability is a transdiagnostic or a disorder-specific mechanism. Current evidence cannot answer this question, but investigating psychopathological mechanisms in everyday life across disorders is a promising approach to enhance validity and specificity of mental health diagnoses.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Ambulatory assessment study of the specificity of affective instability in borderline personality disorder; 'replicate' here refers to confirming a clinical finding, and the object is psychopathology.
The study examines affective instability across psychiatric disorders, not research practice.
Clinical psychology study of affective instability across diagnostic groups; psychopathology object.
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 | 0.000 |
| 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".