Evidence map›Paper›PMID 32343697›Full record

ArticlePloS one2020

Preoperative assessment system for hand-assisted laparoscopic donor nephrectomy by discriminant analysis.

Kazuhiro Iwadoh, Ichiro Nakajima, Ichiro Koyama, Kosaku Nitta, Shohei Fuchinoue

Open access · goldAbstract readValidation Study
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 95% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

5 authors at 1 institution in 1 country.

Kazuhiro IwadohDepartments of Surgery III, Tokyo Women's Medical University, Tokyo, Japan.
Ichiro NakajimaDepartments of Surgery III, Tokyo Women's Medical University, Tokyo, Japan.
Ichiro KoyamaDepartments of Surgery III, Tokyo Women's Medical University, Tokyo, Japan.
Kosaku NittaDepartments of Nephrology, Tokyo Women's Medical University, Tokyo, Japan.ORCID 0000-0003-0508-7830
Shohei FuchinoueDepartments of Surgery III, Tokyo Women's Medical University, Tokyo, Japan.
Tokyo Women's Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We developed a preoperative assessment system to predict surgical workload in hand-assisted laparoscopic donor nephrectomy (HALDNx) using the normal-based linear discriminant rule (NLDR). A total of 128 cases of left HALDNx performed by a single operator were used as training data. Surgical workload was measured by operative time. The optimized model had 9 explanatory variables: age, total protein, total cholesterol, number of renal arteries (numberRA), 4 variables of perinephric fat (PNF), and thickness of subcutaneous fat. This model was validated using cross-validation and the .632 estimator to estimate discrimination rates with future test data. PNF and numberRA were the predominant factors affecting workload followed by the computed tomography value of PNF, body weight, and male sex. The estimated accuracy of the prediction system was 94.6%. The complication rate was 9.38% and did not correlate with surgical workload. We also made our program available online for constructing assessment functions from other cohort data. In conclusion, the surgical workload of HALDNx could be predicted with PNF and numberRA as the dominant risk factors.

Indexed as

Models, StatisticalAdultAgedBody Mass IndexDiscriminant AnalysisFemaleHand-Assisted LaparoscopyHumansKidneyLearning CurveLiving DonorsMaleMiddle AgedNephrectomyOperative TimePatient Safety

Identifiers

PMID32343697
PMCPMC7188199
OpenAlexW3018464281

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.