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Record W2132168868 · doi:10.1177/0269881115602487

Clinical relevance of nalmefene versus placebo in alcohol treatment: Reduction in mortality risk

2015· review· en· W2132168868 on OpenAlexaff
Michael Roerecke, Per Soelberg Sørensen, Philippe Laramée, Nora Rahhali, Jürgen Rehm

Bibliographic record

VenueJournal of Psychopharmacology · 2015
Typereview
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsNalmefenePlaceboMedicineAbstinenceRandomized controlled trialAlcohol dependenceInternal medicineAlcoholNaltrexonePsychiatryBiology

Abstract

fetched live from OpenAlex

Reduction of long-term mortality risk, an important clinical outcome for people in alcohol dependence treatment, can rarely be established in randomized controlled trials (RCTs). We calculated the reduction in all-cause mortality risk using data from short-term (6 and 12 months) double-blind RCTs comparing as-needed nalmefene treatment to placebo, and mortality risks from meta-analyses on all-cause-mortality risk by reduction of drinking in people with alcohol dependence. A reduction in drinking in the RCTs was defined by shifts in drinking risk levels established by the European Medicines Agency. Results showed that the reduction of drinking in the nalmefene group was associated with a reduction in mortality risk by 8% (95% CI: 2%, 13%) when compared to the placebo group. Sensitivity analyses confirmed a significant effect. Thus comparing the difference between nalmefene and placebo in reduction in drinking levels with results on all-cause mortality risk from meta-analyses indicated a clinically relevant reduction in mortality risk. Given the high mortality risk of people with alcohol dependence, abstinence or a reduction in drinking have been shown to reduce mortality risk and should be considered treatment goals.

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)0.004
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.420
GPT teacher head0.606
Teacher spread0.186 · 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
GenreReview

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

Citations23
Published2015
Admission routes1
Has abstractyes

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