The use of buprenorphine/naloxone to treat borderline personality disorder: a case report
Bibliographic record
Abstract
BACKGROUND: Using traditional pharmacotherapy to treat Borderline Personality Disorder (BPD) such as mood stabilizers and second-generation antipsychotics has a lack of supporting evidence. Buprenorphine/Naloxone (BUP/N), a combination medication consisting of a partial opioid agonist, and a full opioid antagonist, is an effective treatment for opioid use disorder. It has also been found effective for treatment-resistant mood disorders. Previous studies suggest a relationship between BPD and endogenous opioids, therefore our case report investigates the effect of BUP/N on a patient diagnosed with BPD. CASE PRESENTATION: A 26-year-old female diagnosed with BPD, having recurrent visits to the emergency department (ED) for self-harm/suicidality was treated with BUP/N. Usage of crisis services, ED visits, and hospital admissions were tracked from 15 months prior to BUP/N to 15 months after using BUP/N. Since starting BUP/N, the length and frequency of mental health-related hospital admissions decreased drastically, as did the number of times that she reached out to community crisis services. Since the dosing adjustment to 6 mg in Oct 2020, there have been no calls to the community crisis lines. CONCLUSIONS: We suggest pharmacological treatment targeting BPD as a disorder of distress tolerance and self-soothing mediated by the opioid system is an effective individual healing attempt. An important note is that this patient did not use opioids prior to BUP/N and had never been diagnosed with an opioid use disorder. However, she exhausted multiple other pharmacologic therapies and was open to trying whatever was available to improve her quality of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".