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Record W3128381200 · doi:10.1097/adm.0000000000000803

Hospital-based Buprenorphine/Naloxone Initiation in a Patient With Limited Communication Abilities: A Case Report

2021· article· en· W3128381200 on OpenAlexaff
Julia Milden, Piper Dickhout, Seonaid Nolan

Bibliographic record

VenueJournal of Addiction Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsSt. Paul's HospitalMichael Smith Health Research BCBritish Columbia Centre on Substance Use
Fundersnot available
KeywordsBuprenorphineMedicine(+)-NaloxoneOpioid use disorderStimulantAphasiaOpioidAnesthesiaStroke (engine)AgonistPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

North America's opioid crisis highlights the profound need for low-barrier access to opioid agonist therapy (OAT) for the treatment of opioid use disorder (OUD). This case describes a 33-year-old female with a history of opioid and stimulant use disorder admitted to hospital after a left middle cerebral artery ischemic stroke with resulting aphasia. After medical stabilization, she consented to buprenorphine/naloxone through limited verbal and written communication. After 14 days of titration, she was stabilized on 14 to 3.5 milligrams of buprenorphine/naloxone daily. Buprenorphine/naloxone initiation and titration is patient-based and symptom guided, typically requiring active communication between the patient and care provider. This case illustrates successful buprenorphine/naloxone initiation in a patient with limited receptive and expressive language abilities, emphasizing the feasibility of offering this medication to individuals with limited communication to ensure timely access to evidence-based OUD treatment.

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)0.001
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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