ArticleFrontiers in oncology2025
Pre-operative T-stage discrimination in gallbladder cancer using machine learning and DeepSeek-R1.
Article in Frontiers in 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Gallbladder cancer (GBC) frequently exhibits non-specific early symptoms, delaying diagnosis. This study (i) assessed whether routine blood biomarkers can distinguish early T stages via machine learning and (ii) compared the T-stage discrimination performance of a large language model (DeepSeek-R1) when supplied with (a) radiology-report text alone versus (b) radiology-report text plus blood-biomarker values. Methods: We retrospectively analyzed 232 pathologically confirmed GBC patients treated at Lishui Central Hospital between 2023 and 2024 (T1, Results: Biomarker-based machine-learning models yielded uniformly poor T-stage discrimination. Without SMOTE, individual models such as XGBoost achieved an AUROC of 0.508 on the independent test set, while recall for the T1 class remained low (e.g., 14.3% for some models), indicating performance near random chance. Applying SMOTE to the training data produced statistically significant gains in cross-validation (CV) accuracy for several models (e.g., XGBoost CV Acc. 0.71 → 0.80, Conclusions: The evaluated blood biomarkers did
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.