Back

DBS for substance use disorders? An exploratory qualitative study of perspectives of people currently in treatment

Versalovic, E.; Klein, E.; Goering, S.; Ngo, Q.; Gliske, K.; Boulicault, M.; Specker Sullivan, L.; Thomas, M. J.; Widge, A.

2022-04-25 addiction medicine
10.1101/2022.04.21.22273594 medRxiv
Show abstract

ObjectiveWhile previous studies have discussed the promise of deep brain stimulation (DBS) as a possible treatment for substance use disorders (SUDs) and collected researcher perspectives on possible ethical issues surrounding it, none have consulted those with SUDs themselves. We addressed this gap by interviewing those with SUDs. MethodsParticipants viewed a short video introducing DBS, followed by a 1.5 hour semi-structured interview on their experiences with SUDs and their perspective on DBS as a possible treatment option. Interviews were analyzed by multiple coders who iteratively identified salient themes. ResultsWe interviewed 20 people in 12-step based, inpatient treatment programs (10 [50%] white/Caucasian, 7 Black/African American [35%], 2 Asian [10%], 1 Hispanic/Latino [5%], and 1 [5%] Alaska Native/American Indian; 11 [45%] women). Interviewees described a variety of barriers they currently faced through the course of their disease that mirrored barriers often associated with DBS (stigma, invasiveness, maintenance burdens, privacy risks) and thus made them more open to the possibility of DBS as a future treatment option. ConclusionsIndividuals with SUDs gave relatively less weight to surgical risks and clinical burdens associated with DBS than previous surveys of provider attitudes anticipated. These differences derived largely from their experiences living with an often-fatal disease and encountering limitations of current treatment options. These findings support the study of DBS as a treatment option for SUDs, with extensive input from people with SUDs and advocates.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.