Evidence map›Paper›PMID 38699367›Full record

ArticleResearch square2024

A machine learning approach to predict

Matt Nagle, Hannah Conroy Broderick, Adrian Buganza Tepole, Michael Fop, Aisling Ní Annaidh

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 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

5 · Who and what money

Authors and funding

5 authors.

Matt NagleSFI Centre for Research Training in Foundations of Data Science, University College Dublin, Belfield, Dublin 4, Ireland.
Hannah Conroy BroderickSchool of Mechanical and Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
Adrian Buganza TepoleSchool of Mechanical Engineering, Purdue University, West Lafayette, USA.
Michael FopSchool of Mathematics and Statistics, University College Dublin, Belfield, Dublin 4, Ireland.
Aisling Ní AnnaidhSchool of Mechanical and Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland.

Funding

Multi-Scale Modeling to Predict Long-Term Growth and Remodeling of Skin in Response to StretchR01AR074525 · NIAMS · PURDUE UNIVERSITY · PI BUGANZA TEPOLE, ADRIAN · 2019 to 2023
$2.7M
NIAMS NIH HHS R01 AR074525
6 · The paper itself

Abstract

Since their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Currently, however, the sole quantitative method to assess skin growth requires skin excision. Thus, in the context of patient outcomes, a machine learning method which uses non-invasive measurements to predict

Indexed as

artificial neural networkelastic wave propagationfinite element simulationmachine learningskin growthtissue expansion

Identifiers

PMID38699367
PMCPMC11065084

What OpenQuestion 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 OpenQuestion graph.