ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Survivorship Navigator: Personalized Survivorship Care Plan Generation using Large Language Models.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Mapping Support-Seeking After Cancer Treatment: A Co-Designed Model of Triggers, Timing and Support Pathways in Young People with Lived Experience of Cancer.Current oncology (Toronto, Ont.) · 2026Article
- Survivorship Navigator: Personalized Survivorship Care Plan Generation using Large Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer survivorship care plans (SCPs) are critical tools for guiding long-term follow-up care of cancer survivors. Yet, their widespread adoption remains hindered by the significant clinician burden and the time- and labor-intensive process of SCP creation. Current practices require clinicians to extract and synthesize treatment summaries from complex patient data, apply relevant survivorship guidelines, and generate a care plan with personalized recommendations, making SCP generation time-consuming. In this study, we systematically explore the potential of large language models (LLMs) for automating SCP generation and introduce Survivorship Navigator, a framework designed to streamline SCP creation and enhance integration with clinical systems. We evaluate our approach through automated assessments and a human expert study, demonstrating that Survivorship Navigator outperforms baseline methods, producing SCPs that are more accurate, guideline-compliant, and actionable.
Indexed as
Identifiers
41726400PMC12919410What 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.