Evidence map›Paper›PMID 41062537›Full record

ArticleScientific reports2025

Insights into the adoption of innovative clinical trials across therapeutic areas using clinical trials registry data and large Language models.

Danila Azzolina, Vittorio Scisciola, Luca Vedovelli, Domenico Iervolino, Mohd Rashid Khan, Rosanna Irene Comoretto, Martino Belvederi Murri, Dario Gregori

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Danila AzzolinaBiostatistics and Clinical Trial Methodology Unit, Clinical Research Center DEMeTra, Department of Translational Medicine, University of Naples Federico II, 80138, Naples, Italy. danila.azzolina@unina.it.
Vittorio ScisciolaUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Luca VedovelliUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Domenico IervolinoUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Mohd Rashid KhanUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Rosanna Irene ComorettoDepartment of Public Health and Paediatrics, University of Turin, Turin, Italy.
Martino Belvederi MurriDepartment of Neuroscience and Psychiatry, University of Ferrara, Ferrara, Italy.
Dario GregoriUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Innovative clinical trial designs, such as adaptive and Bayesian methodologies, have gained traction as solutions to the challenges of traditional trials, including their high costs and complex regulations. When they adhere to relevant ethical and regulatory requirements, these designs can improve efficiency, flexibility, and ethical standards. However, their application outside of oncology, particularly in fields such as neuroscience and rare diseases, remains underexplored. We analyzed data from ClinicalTrials.gov for interventional trials registered between 2005 and 2024. The trials were classified as innovative or traditional using a keyword-based algorithm. Therapeutic areas were identified using a large language model (LLM), with classification accuracy evaluated using a random sample of 2,000 trials. Of the 348,818 trials, 5827 were classified as innovative, with prevalence in neuroscience and rare diseases. These designs were predominantly observed in early-phase trials and pediatric research, with limited representation in elderly-focused or sex-specific studies. Innovative trial adoption has grown since 2011, spurred by regulatory advancements and increased funding from scientific networks and the National Institutes of Health. Survival analysis revealed that innovative trials tend to remain active for longer than traditional trials; however, this trend varies across different medical disciplines. LLM demonstrated a classification accuracy of 94.6% (95%CI = 93.6%-95.5%), supporting its utility for trial categorization. The rise in innovative clinical trial designs reflects a shift toward addressing complex challenges in neuroscience, rare diseases, and other therapeutic areas. Although these designs show promise in improving trial efficiency and patient outcomes, their success depends on rigorous planning and adherence to regulatory standards. Advancing LLM-based tools can further optimize clinical trial monitoring by tailoring research in trial settings and therapeutic fields.

Indexed as

Clinical Trials as TopicRegistriesBayes TheoremHumansLanguageLarge Language ModelsResearch DesignClinicalTrials.govHealth equityInnovative clinical trialsLLM (Large language model)NeurosciencePsychiatry

Identifiers

PMID41062537
PMCPMC12508068

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.