The use of cellphone based automated pupillometry to quantify opioid withdrawal in patients receiving treatment for opioid use disorder.
Evonko CJ; Vigilante KC; Leli NB; Pond RS Jr; Chaney MW; Ludington JD; Van Leuven EG; Washington WD · 2026 · Journal of substance use and addiction treatment
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Introduction Overdose deaths attributed to opioid misuse resulted in over 50,000 deaths in 2024. Opioid agonist therapies such as methadone and buprenorphine are effective for the treatment of opioid use disorder (OUD). Yet, patient…
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke
Negative / Null Result ReportDuodenal Infusion of Donor Feces for Recurrent Clostridium difficile
Negative / Null Result ReportStenting versus Endarterectomy for Treatment of Carotid-Artery Stenosis
Negative / Null Result ReportEffects of Combination Lipid Therapy in Type 2 Diabetes Mellitus
Replication FailureA Randomized Trial of Bevacizumab for Newly Diagnosed Glioblastoma
Negative / Null Result ReportSpironolactone for Heart Failure with Preserved Ejection Fraction
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: Europe PMC · DOI 10.1016/j.josat.2026.209927