ArticleAnnals of surgical oncology2025
Developing a Novel Artificial Intelligence Framework to Measure the Balance of Clinical Versus Nonclinical Influences on Posthepatectomy Length of Stay.
Article in Annals of surgical oncology, 2025. 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.
- Explainable Boosting Machine Predicting Length of Stay After Liver Surgery in Patients with Colorectal Liver Metastases.Cancers · 2026Article
- Critical Methodological Considerations for Quantifying Non-clinical Drivers of Post-hepatectomy Length of Stay.Annals of surgical oncology · 2025Article
- ASO Author Reflections: AI Unraveling Clinical Versus Nonclinical Effects on Post-hepatectomy LOS.Annals of surgical oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLength of stay (LOS) is a key indicator of posthepatectomy care quality. While clinical factors influencing LOS are identified, the balance between clinical and nonclinical influences remains unquantified. We developed an artificial intelligence (AI) framework to quantify clinical influences on LOS and infer the impact of hard-to-measure nonclinical factors.
methodsPatients from the 2017 to 2021 ACS NSQIP Hepatectomy-Targeted database were stratified into major and minor hepatectomy groups. A three-tiered model-multivariable linear regression (MLR), random forest (RF), and extreme gradient boosting (XGBoost)-was developed to evaluate the effect of 52 clinical variables on LOS. Models were fine-tuned to maximize clinical variables' explanatory power, with residual unexplained variability attributed to nonclinical factors. Model performance was measured using R
resultsA total of 21,039 patients (mean age: 60 years; 51% male) were included: 70% underwent minor resection (mean LOS: 5.0 days), and 30% underwent major resection (mean LOS: 6.9 days). Random forest had the best performance, explaining 75% of LOS variability for both groups (R
conclusionsThis study is the first to measure the true influence of clinical factors on posthepatectomy LOS, showing that they explain 75% of the variability. Furthermore, it indirectly evaluated the overall impact of hard-to-measure nonclinical factors, revealing that they account for 25% of LOS.
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.