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TRUVACE RECORD VERSION
record: TRV-2026-0918
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-28T06:07:07.585474Z
status: published
lens: g_space
sector: health
headline: TrialScout links published results to trial registrations using a large language model
dek: Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find them. Summary results of clinical trials are often not published in trial registries, and publications containing trial results are often not explicitly linked to their respective trial registrations. Finding these results is important to researchers, systematic reviewers, research funders, regulators, clinical practitioners, and patients. We developed TrialScout, a computer program that uses a large langua…
gain_title: TrialScout using a large language model matched registered trials to result publications with 92.5% sensitivity and located publications for 63.6% of a 9,600-trial sample, accelerating locating trial results.
problem_title: (none)
trace_subject: (none)
gain_reading: TrialScout using a large language model matched registered trials to result publications with 92.5% sensitivity and located publications for 63.6% of a 9,600-trial sample, accelerating locating trial results.
gain_evidence: TrialScout had a sensitivity of 92.5% and a specificity of 81.2% compared to human coders. | When used on 9,600 sampled trials in ClinicalTrials.gov, TrialScout found result publications for 6,110 (63.6%) of trials.
problem_reading: (none)
problem_evidence: (none)
quick_read: On 2026-08-26, a peer-reviewed study described TrialScout, a program that uses a large language model to link ClinicalTrials.gov registrations to PubMed result publications. Tested against prior human-coded datasets, it achieved 92.5% sensitivity and 81.2% specificity, and when applied to 9,600 sampled completed or terminated trials it identified publications for 6,110 trials.

The work matters because locating trial results is critical for evidence synthesis, regulatory oversight, and clinical decision-making, and automation could speed monitoring of reporting practices. Uncertainty remains about true accuracy because no gold standard exists, and the reported 61.5% human-error rate in disagreements suggests evaluation itself is imperfect.
limitation: 
tag: Evidence-backed gain
key_points: TrialScout was developed to match clinical trials registered on ClinicalTrials.gov with corresponding result publications indexed in PubMed. | Performance was evaluated against human-coded matches from previous studies of results reporting rates. | Manual review of 200 disagreements found a majority (123/200, 61.5%, 95% CI, 54.4-68.3%) were due to human errors. | Cross-sectional analysis applied TrialScout to a random sample of 9,600 completed or terminated trials.
rundown: Researchers built TrialScout to address the gap where summary results are often not published in registries and publications are often not explicitly linked to registrations, affecting researchers, systematic reviewers, funders, regulators, practitioners, and patients.

Evaluation compared TrialScout to human-coded matches, then applied it to 9,600 completed or terminated trials from ClinicalTrials.gov, with manual review of 200 discordant cases to adjudicate errors.
sources:
- peer_reviewed | Journal of Clinical Epidemiology | https://doi.org/10.1016/j.jclinepi.2026.112484 | 2026-08-26
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