ArticleJournal of managed care & specialty pharmacy2026
Natural language processing to develop a standardized lexicon for precision medicine in oncology.
Article in Journal of managed care & specialty pharmacy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Payer coverage principles for precision medicine biomarker testing in oncology.Journal of managed care & specialty pharmacy · 2026Article
- Advancing precision medicine access in oncology: Foundational considerations for managed care stakeholders.Journal of managed care & specialty pharmacy · 2026Article
- Key considerations when assessing clinical utility for biomarker testing: A checklist.Journal of managed care & specialty pharmacy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine terminology is increasingly used across clinical, laboratory, payer, regulatory, and policy settings; however, distinct terminology may be applied interchangeably or inconsistently across stakeholders, and foundational concepts may not be uniformly understood across multidisciplinary audiences with varying levels of expertise. This misalignment may create confusion for stakeholders as they attempt to navigate guideline recommendations and payer coverage policies, potentially impacting patient access to testing and treatment. In 2025, AMCP held a 2-day partnership forum on the topic of advancing precision medicine in oncology. Forum participants recommended development of a consolidated lexicon of commonly used precision medicine terms to support clearer communication and foundational understanding across stakeholders and to clarify distinctions between related concepts. Nineteen definitions were ultimately generated using a natural language processing process, an artificial intelligence approach used to extract information and derive meaning from large text samples. A comprehensive literature review was first conducted to develop an index of publicly available precision medicine terminology sources. Natural language processing-based textual analysis was then used to systematically evaluate terminology usage patterns across the index, including both consistent and inconsistent usage of terms across sources. Draft definitions generated through this process were subsequently reviewed and refined by forum participants. The resulting lexicon included relevant terms related to (1) foundational concepts in precision medicine; (2) biological foundations and molecular variation; (3) biomarker and molecular testing; (4) testing technologies, specimen collection, and regulatory classification; and (5) testing approach. The objective of this work was to organize commonly used precision medicine terms into a single reference framework, a foundational precision medicine reference for managed care stakeholders, and to clarify distinctions between related concepts where appropriate.
Indexed as
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.