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Record W2946051957

Buprenorphine in the emergency department: Randomized clinical controlled trial of clonidine versus buprenorphine for the treatment of opioid withdrawal.

2019· article· en· W2946051957 on OpenAlexaffabout
Anita Srivastava, Meldon Kahan, Irene Njoroge, Leeor Sommer

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBuprenorphineMedicineClonidineEmergency departmentRandomized controlled trialOpioidAddictionAnesthesiaMedical prescriptionAgonistAttendancePsychiatryInternal medicinePharmacology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare buprenorphine to clonidine for the treatment of opioid withdrawal in the emergency department (ED) and to study the effect assigned treatment medication had on longer-term addiction treatment outcomes. DESIGN: Randomized controlled trial. SETTING: Toronto, Ont. PARTICIPANTS: Twenty-six patients presenting to the ED while in opioid withdrawal or soon to be in opioid withdrawal. MAIN OUTCOME MEASURES: Patients were randomized to receive either clonidine or buprenorphine treatment. Both groups also received a corresponding discharge prescription and information on how to follow up in the addictions rapid access clinic (RAC) within a few days. Participants were followed for 1 month with respect to attendance at the RAC and to opioid agonist treatment status. Outcome measures included attendance at the RAC within 5 days of the initial ED visit and opioid agonist treatment status at 1 month (as determined by clinic attendance or self-report during a follow-up telephone interview). RESULTS: = .011). CONCLUSION: When opioid withdrawal is treated with buprenorphine in the ED, patients are more likely to be receiving opioid agonist treatment and connected with addiction treatment 1 month later. TRIAL REGISTRATION NUMBER: NCT03174067 (ClinicalTrials.gov).

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)0.003
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.030
GPT teacher head0.310
Teacher spread0.280 · 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 designRandomized trial
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

Citations25
Published2019
Admission routes2
Has abstractyes

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