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Record W4402585657 · doi:10.1093/milmed/usae416

Buprenorphine for the Treatment of Military-related PTSD With Treatment-resistant Depression and Unexpected Benefit for Chronic Pain: Case Report

2024· article· en· W4402585657 on OpenAlexaff
Anna Skorzewska, William Younger, Kylie S. Dempster, Anthony Nazarov, J. Don Richardson

Bibliographic record

VenueMilitary Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsSt Joseph's Health CareMcMaster UniversityLawson Health Research InstituteUniversity of TorontoWestern University
Fundersnot available
KeywordsBuprenorphineDepression (economics)MedicineChronic painPsychiatryMilitary medicineOpioidInternal medicine

Abstract

fetched live from OpenAlex

The prevalence of treatment-resistant depression within global and military populations highlights the need for novel treatment approaches beyond monoamine neurotransmitter modulators. Buprenorphine (BUP), a semi-synthetic partial opioid agonist, is approved for the treatment of opioid use disorder and has shown promise in treating both depression and chronic pain. This case report discusses the use of transdermal BUP in treating a 36 year-old man with treatment-resistant depression with prominent anhedonia, military-related posttraumatic stress disorder, and chronic pain because of barosinusitis. Significant reductions in anxious and depressive symptoms, including in anhedonia, were observed with lasting effects. An unexpected finding was the discontinuation of prescribed hydromorphone for pain, suggesting the potential unique benefit of BUP in treating chronic pain and treatment resistant depression comorbidities. These findings implicate the diverse beneficial potential of BUP in psychiatric treatments for military populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.285
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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