Evidence mapPaperPMID 39630321Full record

Article3D printing in medicine2024

Development and evaluation of 3D-printed tumor palpation models for surgical training and patient education.

Haruna Katori, Atsushi Fushimi, Soichiro Fujimura, Rei Kudo, Makiko Kamio, Hiroko Nogi

Abstract read
In one paragraph

Article in 3D printing in medicine, 2024. 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

6 authors.

Haruna KatoriDepartment of Medicine, The Jikei University School of Medicine, Tokyo, Japan. harunyan327@docomo.ne.jp.
Atsushi FushimiDepartment of Breast and Endocrine Surgery, The Jikei University School of Medicine, Tokyo, Japan. fushimi@jikei.ac.jp.
Soichiro FujimuraDivision of Innovation for Medical Information Technology, The Jikei University School of Medicine, Tokyo, Japan.
Rei KudoDivision of Cancer RNA Research, National Cancer Center Research Institute, Tokyo, Japan.
Makiko KamioDepartment of Breast and Endocrine Surgery, The Jikei University School of Medicine, Tokyo, Japan.
Hiroko NogiDepartment of Breast and Endocrine Surgery, The Jikei University School of Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer screening is implemented as part of governmental healthcare policy in many countries. While breast imaging tests contribute to reducing mortality, some breast cancers may emerge between these screenings. Consequently, it is crucial for women to be vigilant about any changes in their breasts to facilitate the early detection of breast cancer. Recently, the application of 3-dimensional printing technology in the medical field has expanded, including uses in medical imaging and surgical training. In this study, we developed 3D-printed palpation models for breast tumor detection and surveyed seven surgeons specializing in breast care to evaluate the usability of the models. As a result of the survey, we created a model that obtained a maximum mean rating of 7.1(maximum rating 10, minimum rating 3) on the item 'How accurately does the model simulate the feel of a real tumor?' on a scale from 1 to 10. Although there is some variation in the average value, through this study, we found that it is possible to create a model that is quite close to the actual tumor depending on the materials and shape of the models. Our findings demonstrated the potential use of personalized models both in medical trainee and patient education.

Indexed as

3D printing technologyBreast cancerMedical educationPalpations modelsTumor detection

Identifiers

PMID39630321
PMCPMC11616315

What Socratic holds

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LicenceCC BY-NC-ND
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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.