Longitudinal Diagnostic Efficiency of DSM-IV Criteria for Borderline Personality Disorder: A 2-Year Prospective Study
Bibliographic record
Abstract
OBJECTIVE: To examine the longitudinal diagnostic efficiency of the DSM-IV criteria for borderline personality disorder (BPD). METHODS: At baseline, we used semistructured diagnostic interviews to determine criteria and diagnoses; blinded assessments were performed 24 months later with 550 participants. Diagnostic efficiency indices (specifically, conditional probabilities, total predictive power, and kappa) were calculated for each criterion determined at baseline, with the independent BPD diagnosis at follow-up used as the standard. RESULTS: Longitudinal diagnostic efficiencies for the BPD criteria varied, with the criteria of suicidality or self-injury and unstable relationships demonstrating the most predictive utility. CONCLUSIONS: BPD criteria differ in their predictive utility for the diagnosis of BPD when considered longitudinally. These findings have implications both for clinicians who are considering diagnoses and for researchers concerned with forthcoming revisions of our nosological system.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".