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Record W2775634389 · doi:10.5055/jom.2017.0415

Do abuse deterrent opioid formulations work?

2017· review· en· W2775634389 on OpenAlexaboutno aff
Richard C. Dart, Janetta Iwanicki, Nabarun Dasgupta, Theodore J. Cicero, Sidney H. Schnoll

Bibliographic record

VenueJournal of Opioid Management · 2017
Typereview
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsHydrocodoneOxycodoneMedicineSubstance abuseOpioidAddictionMorphineMethadonePsychiatryPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: We performed a systematic review to answer the question, "Does the introduction of an opioid analgesic with abuse deterrent properties result in reduced overall abuse of the drug in the community?" DESIGN: We included opioid analgesics with abuse deterrent properties (hydrocodone, morphine, oxycodone) with results restricted to the metasearch term "delayed onset," English language, use in humans, and publication years 2009-2016. All articles that contained data evaluating misuse, abuse, overdose, addiction, and death were included. The results were categorized using the Bradford-Hill criteria. RESULTS: We included 44 reports: hydrocodone (n = 7), morphine (n = 5), or oxycodone (n = 32) with Food and Drug Administration-approved Categories 1, 2, or 3 abuse deterrent labeling. The data currently available support the Hill criteria of strength (effect size), consistency (reproducibility), temporality, plausibility, and coherence. There was insufficient or no information available for the criteria of biological gradient, experiment, and analogy. We also assessed confounding factors and bias, which indicated that both were present and substantial in magnitude. CONCLUSIONS: Our analysis found that only oxycodone extended release (ER) had information available to evaluate abuse deterrence in the community. In Australia, Canada, and the United States, reformulation of oxycodone ER was followed by marked reduction in measures of abuse. The precise extent of reduced abuse cannot be calculated because of heterogeneous data sets, but the reported reductions ranged from 10 to 90 percent depending on the measure and the duration of follow-up.

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)0.000
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 score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.091
GPT teacher head0.393
Teacher spread0.301 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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