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Record W4319062608 · doi:10.1097/adm.0000000000001148

“Macrodosing” Sublingual Buprenorphine and Extended-release Buprenorphine in a Hospital Setting: 2 Case Reports

2023· article· en· W4319062608 on OpenAlexaffabout
Meldon Kahan, Louisa Marion-Bellemare, Julie Samson, Anita Srivastava

Bibliographic record

VenueJournal of Addiction Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsNorthern CollegeCentre for Addiction and Mental HealthWomen's College Hospital
Fundersnot available
KeywordsBuprenorphineMedicineFentanyl(+)-NaloxoneSublingual administrationAnesthesiaSedationEmergency departmentOpioidEmergency medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe 2 case reports in which high-dose administration of sublingual buprenorphine/naloxone quickly stabilized fentanyl users who presented to the hospital. To discuss how early administration of extended-release buprenorphine, before the patient is discharged, may improve retention rates for outpatient buprenorphine treatment. METHODS: Two case reports of fentanyl users presented to the emergency department at the general hospital in Timmins, Canada are described. They were rapidly stabilized on high-dose sublingual buprenorphine/naloxone and then transitioned within 24 to 36 hours to buprenorphine extended-release subcutaneous injection. RESULTS: In both cases, their withdrawal symptoms quickly resolved, without sedation or precipitated withdrawal. Both patients followed up with the outpatient clinic for another injection of extended-release buprenorphine. CONCLUSIONS: High-dose sublingual buprenorphine/naloxone followed by early administration of extended-release buprenorphine quickly and safely relieved withdrawal symptoms in 2 fentanyl users who presented to the hospital emergency department. This novel approach shows promise in improving treatment retention rates for patients using fentanyl. Further research is required to evaluate the safety and effectiveness of this approach.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.285
Teacher spread0.274 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations15
Published2023
Admission routes2
Has abstractyes

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