Evidence map›Paper›PMID 36348752›Full record

ArticlePlastic and reconstructive surgery. Global open2022

Breast Decisions: Recommender System for Appearance Counseling about Breast Reconstruction.

Krista M Nicklaus, Audrey Cheong, Urmila Sampathkumar, Jun Liu, Deepti Chopra, Aubri Hoffman, Fatima A Merchant, Summer E Hanson, Mia K Markey, Gregory P Reece

Open access · goldAbstract read
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 2022. 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
0.6field-weighted citation impact, top 30% 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, 4 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

10 authors at 2 institutions in 1 country.

Krista M NicklausDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, Tex.
Audrey CheongDepartment of Electrical and Computer Engineering, University of Houston, Houston, Tex.
Urmila SampathkumarDepartment of Computer Science, University of Houston, Houston, Tex.
Jun LiuDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, Tex.
Deepti ChopraDepartment of Psychiatry, The University of Texas MD Anderson Cancer Center, Houston, Tex.
Aubri HoffmanThe Value Institute for Health and Care, Dell Medical School, The University of Texas at Austin, Austin, Tex.
Fatima A MerchantDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, Tex.
Summer E HansonSection of Plastic and Reconstructive Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illi.
Mia K MarkeyDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, Tex.
Gregory P ReeceDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, Tex.
The University of Texas at Austin · USThe University of Texas MD Anderson Cancer Center · US

Funding

3-D modeling-based decision support for optimizing quality of life following breast reconstructionR01CA203984 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI MARKEY, MIA K, MERCHANT, FATIMA AZIZ · 2016 to 2020
$3.4M
3-D Computer Modeling for Optimizing Body Image following Breast ReconstructionR01CA143190 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI MARKEY, MIA K · 2010 to 2014
$2.9M
NCI NIH HHS R01 CA143190NCI NIH HHS R01 CA203984
6 · The paper itself

Abstract

Appearance counseling is an important component of the consent process for breast reconstruction. The purpose of appearance counseling is to help the patient form realistic expectations of what she might look like after breast reconstruction. In this article, we introduce a recommender system, "BreastDecisions," for appearance counseling that suggests photographs of previous patients that are tailored to a specific patient to help her form realistic expectations of her own reconstruction. Methods: We present user specifications and algorithm parameters needed to incorporate the recommender system into the appearance counseling workflow. We demonstrate the system for a common counseling scenario using a knowledgebase of previous breast reconstruction patients. The medical appropriateness of the recommended photographs for use in appearance counseling was evaluated by experts using a four-point rating system. Results: The recommender system presents photographs that are medically appropriate for counseling a specific patient, depicts typical outcomes, and adapts to a variety of clinical workflows. For each of 33 patients taken as examples of breast reconstruction patients, we used the system to identify photographs for appearance counseling. The baseline average medical appropriateness of the recommended photographs was between mostly appropriate (some explanation needed) and medically appropriate (minimal explanation needed). We demonstrate filtering and ranking steps to reduce the number of recommended photographs and increase the average medical appropriateness. Conclusions: Our recommender system automatically suggests photographs of previous breast reconstruction patients for use in counseling a patient about appearance outcomes. The system is patient-specific and customizable to a particular surgeon's practice.

Identifiers

PMID36348752
PMCPMC9633089
OpenAlexW4308683246

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.