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Record W4395692418 · doi:10.1186/s13063-024-08104-x

Assessing fragility of statistically significant findings from randomized controlled trials assessing pharmacological therapies for opioid use disorders: a systematic review

2024· review· en· W4395692418 on OpenAlexaff
Leen Naji, Brittany B. Dennis, Myanca Rodrigues, Monica Bawor, Alannah Hillmer, Caroul Chawar, Eve Deck, Andrew Worster, James Paul, Lehana Thabane, Zainab Samaan

Bibliographic record

VenueTrials · 2024
Typereview
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsWestern UniversityUniversity of TorontoMcMaster UniversityImpactSt. Joseph’s Healthcare HamiltonUniversity of British ColumbiaHealth Sciences Centre
Fundersnot available
KeywordsMedicineRandomized controlled trialOpioidMeta-analysisClinical trialFragilityInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The fragility index is a statistical measure of the robustness or "stability" of a statistically significant result. It has been adapted to assess the robustness of statistically significant outcomes from randomized controlled trials. By hypothetically switching some non-responders to responders, for instance, this metric measures how many individuals would need to have responded for a statistically significant finding to become non-statistically significant. The purpose of this study is to assess the fragility index of randomized controlled trials evaluating opioid substitution and antagonist therapies for opioid use disorder. This will provide an indication as to the robustness of trials in the field and the confidence that should be placed in the trials' outcomes, potentially identifying ways to improve clinical research in the field. This is especially important as opioid use disorder has become a global epidemic, and the incidence of opioid related fatalities have climbed 500% in the past two decades. METHODS: Six databases were searched from inception to September 25, 2021, for randomized controlled trials evaluating opioid substitution and antagonist therapies for opioid use disorder, and meeting the necessary requirements for fragility index calculation. Specifically, we included all parallel arm or two-by-two factorial design RCTs that assessed the effectiveness of any opioid substitution and antagonist therapies using a binary primary outcome and reported a statistically significant result. The fragility index of each study was calculated using methods described by Walsh and colleagues. The risk of bias of included studies was assessed using the Revised Cochrane Risk of Bias tool for randomized trials. RESULTS: Ten studies with a median sample size of 82.5 (interquartile range (IQR) 58, 179, range 52-226) were eligible for inclusion. Overall risk of bias was deemed to be low in seven studies, have some concerns in two studies, and be high in one study. The median fragility index was 7.5 (IQR 4, 12, range 1-26). CONCLUSIONS: Our results suggest that approximately eight participants are needed to overturn the conclusions of the majority of trials in opioid use disorder. Future work should focus on maximizing transparency in reporting of study results, by reporting confidence intervals, fragility indexes, and emphasizing the clinical relevance of findings. TRIAL REGISTRATION: PROSPERO CRD42013006507. Registered on November 25, 2013.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.213
metaresearch head score (Gemma)0.631
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.787
Threshold uncertainty score0.971

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2130.631
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0210.028
Bibliometrics0.0400.027
Science and technology studies0.0020.004
Scholarly communication0.0080.009
Open science0.0050.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.285
GPT teacher head0.520
Teacher spread0.236 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
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

Citations3
Published2024
Admission routes1
Has abstractyes

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