ReviewJournal of neuro-oncology2025
Using data for novel discoveries in neuro-oncology research.
Review in Journal of neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe study of brain and central nervous system (CNS) tumors has generated and relied upon abundant and diverse data. Robust and comprehensive real-world datasets (RWD) are crucial for understanding risk factors, prognostic factors and clinical outcomes of those diagnosed with these tumors. RWD contains clinically-rich data collected in the context of routine delivery of care and includes an array of variables derived from electronic health records. These data can be leveraged to gain further insight into disease states, treatments and outcomes. METHODS/
resultsHere, we provide a brief overview of considerations when picking a dataset to utilize, and we describe various sources of neuro-oncology data including cancer registries, administrative claims, and commercial RWD.
conclusionsFuture work will undoubtably include the integration of artificial intelligence and machine learning for the analysis of RWD and include linkage to imaging and unstructured clinical data. Unfortunately most RWD sources lack histopathology characteristics which emphasizes the need for linkage data elements that will allow for accurate tumor analyses.
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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.