Articlenpj digital surgery2026
Use of AI agents to assess preoperative frailty in cancer patients.
Article in npj digital surgery, 2026. 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment of preoperative frailty is essential for risk stratification and optimization of patients undergoing major abdominal surgery for hepatobiliary (HPB) and gastrointestinal (GI) malignancies. Large language model (LLM)-based agents may facilitate automated frailty scoring, thereby supporting targeted prehabilitation efforts to improve surgical outcomes. We compared the performance of an agentic LLM system with a single LLM system in calculating frailty scores and classifying frailty status. Using real-world preoperative notes documented within 60 days prior to surgery, we evaluated both approaches. The agentic LLM system outperformed the single LLM system in 4 out of 6 models for a binary frailty classification task and in 5 out of 6 models for generating patient Risk Assessment Index (RAI) scores. The greatest improvements were observed among lower-parameter models (Llama 3.1 8b, Qwen 2.5 7b). These findings suggest than an agentic workflow may enhance frailty assessment performance. Automating frailty score calculation from Electronic Health Record (EHR) data may enhance clinical efficiency and enable targeted prehabilitation strategies for patients with cancer undergoing surgery.
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.