ArticleNPJ precision oncology2025
A prospective pragmatic evaluation of automatic trial matching tools in a molecular tumor board.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of large language models in tumor boards: a systematic review.Oncology reviews · 2026Pooled it
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Automatic prediction of patients early discontinuation in oncology clinical trials.ESMO real world data and digital oncology · 2026Article
- Large language models in oncology: promise, pitfalls, and the path to real-world adoption.ESMO real world data and digital oncology · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- TrialMatchAI: an end-to-end AI-powered clinical trial recommendation system to streamline patient-to-trial matching.Nature communications · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Publicly available trial matching tools can improve the access to therapeutic innovations, but errors may expose to over-solicitation and disappointment. We performed a pragmatic non-interventional prospective evaluation on sequential patients at the Molecular Tumor Board of Centre Leon Berard. During 10 weeks in 2024, we analysed 157 patients with four clinical trial matching tools from the 19 screened: Klineo, ScreenAct, Trialing and DigitalECMT. Each patient had 2.19 trials proposed on average, and 38% had no trials suggested. The mean performances were precision = 0.33, recall = 0.32, AP@3 = 0.45, and NDCG@3 = 0.34. Using all the tools can increase to 26% the clinical trial options. The most frequent error concerned the type of gene variants required by the selection criteria. We showed that using a Large Language Model on the patients' molecular reports could improve the performance by up to 5%. We recommend that experts supervise the results and we advocate for improved technologies.
Identifiers
What OpenQuestion holds
Registered trials
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.