Evidence map›Paper›PMID 42582621›Full record

ArticleAnnals of surgical treatment and research2026

Prediction model for hypertrophied liver volume followed by hemihepatectomy: a retrospective cohort study.

Jiwon Yu, Sunghae Park, Gyu-Seong Choi, Jongman Kim, Namkee Oh, Bogeun Kim, Jinsoo Rhu

Abstract read
In one paragraph

Article in Annals of surgical treatment and research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jiwon YuSungkyunkwan University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0009-0005-3481-5501
Sunghae ParkDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-4383-406X
Gyu-Seong ChoiDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-2545-3105
Jongman KimDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-1903-8354
Namkee OhDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-6594-8973
Bogeun KimDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0009-0001-0360-0832
Jinsoo RhuDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-9809-8525

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to develop a statistical model for predicting hypertrophied future liver remnant volume (HFLRV) in patients with right hemihepatectomy (RHH). Methods: Patients who underwent RHH at Samsung Medical Center between January 2022 and March 2024 were included in this study. Liver volume was calculated from abdomen CT by using a deep learning-based volumetric tool. Results: A total of 98 patients were included. There were 58 and 40 patients whose liver regeneration index was less than 0.8 and 0.8 or more, respectively. The prediction model, which focused on identifying variables associated with HFLRV, included age at diagnosis (P = 0.14), sex (P = 0.01), both preoperative hemi-liver volumes (P < 0.001), and international normalized ratio (P < 0.05). This model demonstrated adjusted R Conclusion: This study developed a linear prediction model for HFLRV and determined which factors can be used to predict HFLRV before RHH.

Indexed as

Computer-assistedHepatectomyLiver failureLiver regeneration

Identifiers

PMID42582621
PMCPMC13457784

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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.