Evidence map›Paper›PMID 28593408›Full record

ArticleSurgical endoscopy2017

A novel model for prediction of pure laparoscopic liver resection surgical difficulty.

Yasushi Hasegawa, Go Wakabayashi, Hiroyuki Nitta, Takeshi Takahara, Hirokatsu Katagiri, Akira Umemura, Kenji Makabe, Akira Sasaki

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
87citing papers in PubMed, 2 pooled it
–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

87 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Advanced robotic liver surgery.Surgical endoscopy · 2026
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  10. Robotic Hepatectomy for Primary Liver Cancer.Annals of surgical oncology · 2026
    Review
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  13. Article
  14. International Benchmark Values for Robotic Right Hepatectomy: Multicenter Study From 22 Expert Centers.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025
    Article
  15. Article
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  20. Article

27 more citing papers are in PubMed but not listed here.

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

8 authors.

Yasushi HasegawaDepartment of Surgery, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka City, Iwate, 020-8505, Japan. hasegawayas@yahoo.co.jp.
Go WakabayashiDepartment of Surgery, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka City, Iwate, 020-8505, Japan.
Hiroyuki NittaDepartment of Surgery, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka City, Iwate, 020-8505, Japan.
Takeshi TakaharaDepartment of Surgery, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka City, Iwate, 020-8505, Japan.
Hirokatsu KatagiriDepartment of Surgery, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka City, Iwate, 020-8505, Japan.
Akira UmemuraDepartment of Surgery, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka City, Iwate, 020-8505, Japan.
Kenji MakabeDepartment of Surgery, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka City, Iwate, 020-8505, Japan.
Akira SasakiDepartment of Surgery, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka City, Iwate, 020-8505, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExtending the clinical indications for laparoscopic liver resection (LLR) should be carefully considered based on a surgeon's experience and skill. However, objective indexes to help surgeons assess the estimated difficulty of LLR are scarce. The aim of our study was to develop the first objective numerical rating scale to predict the surgical difficulty of various LLR procedures.

methodsWe performed a retrospective review of the operative outcomes of 187 patients who underwent a pure LLR. First, the value of preoperative factors for predicting surgical time was evaluated by multivariate linear regression analyses, and a scoring system was constructed. Next, the integrity of our predictive linear model was evaluated against the documented operative outcomes for patients forming our study group.

resultsFour predictive factors were identified and scored based on the weighted contribution of each factor predicting surgical time: extent of resection (scored 0, 2, or 3); location of tumor (scored 0, 1, or 2); obesity (scored 0 or 1); and platelet count (scored 0 or 1). The scores were summed to classify surgical difficulty into three levels: low (total score ≤1); medium (total score 2-3); and high (total score ≥4). Operative outcomes, including surgical time, volume of blood loss, length of hospital stay, and rate of morbidity, were significantly different between the three surgical difficulty levels.

conclusionOur novel model will be useful for surgeons to predict the difficulty of an LLR procedure relative to their own experience and skill.

Indexed as

HepatectomyLaparoscopyOperative TimeAgedBlood Loss, SurgicalFemaleHumansLength of StayLiver NeoplasmsMaleMiddle AgedMultivariate AnalysisObesityPredictive Value of TestsRetrospective StudiesTreatment OutcomeDifficultyHepatectomyLaparoscopicLiver resectionPredictionScoring system

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Read underepoch 390

Registered trials

None linked

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.