Defining Borderline Personality Disorder Impulsivity: Review of Neuropsychological Data and Challenges that Face Researchers
Bibliographic record
Abstract
It has been pointed out that the definition of BPD impulsivity would be improved by incorporating neurobehavioral models in order to bridge the research and the DSM behavioral criterion.Moeller et al. [1] have proposed three neuropsychological diagnostic criterions related to impulsivity in psychiatric disorders: (1) rapid, unplanned reactions to stimuli before complete processing of information; (2) lack of regard for long-term consequences and;(3) decreased sensitivity to negative consequences of behavior.The goal of this paper was to review the neuropsychological literature of BPD impulsivity in line with these neuropsychological diagnostic criterions to verify if the evidence from neuropsychological data and measurements is sufficiently strong to be integrated into the J Psychiatry Psychiatric Disord 2017; 1 (3): 154-176 155 DSM definition of BPD impulsivity.Results of the review highlight some evidence regarding neuropsychological deficits in BPD patients that may be underlying their impulsive self-damaging behaviors.However, at least five methodological challenges are pointed out and need to be addressed before these deficits can be successfully integrated into a definition of BPD impulsivity.Some solutions are proposed to face the main challenges in studying impulsivity in BPD.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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".