Evidence map›Paper›PMID 39974037›Full record

ArticlemedRxiv : the preprint server for health sciences2025

A STRUCTURAL EQUATION MODEL PREDICTS CHRONIC WOUND HEALING TIME USING PATIENT CHARACTERISTICS AND WOUND MICROBIOME COMPOSITION.

Jacob Ancira, Rebecca Gabrilska, Craig Tipton, Clint Miller, Zachary Stickley, Khalid Omeir, Catherine Wakeman, Todd Little, Joseph Wolcott, Caleb D Philips

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. 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

10 authors.

Jacob AnciraDepartment of Biological Sciences, Texas Tech University, Lubbock, USA.
Rebecca GabrilskaDepartment of Surgery, Texas Tech University Health Sciences Center, Lubbock, USA.
Craig TiptonDepartment of Biological Sciences, Texas Tech University, Lubbock, USA.
Clint MillerSouthwest Regional Wound Care Center, Lubbock, USA.
Zachary StickleyDepartment of Educational Psychology and Leadership, Texas Tech University, Lubbock, TX, USA.
Khalid OmeirRTL Genomics, MicroGenDx, Lubbock, Texas, USA.
Catherine WakemanDepartment of Biological Sciences, Texas Tech University, Lubbock, USA.
Todd LittleDepartment of Educational Psychology and Leadership, Texas Tech University, Lubbock, TX, USA.
Joseph WolcottSouthwest Regional Wound Care Center, Lubbock, USA.
Caleb D PhilipsDepartment of Biological Sciences, Texas Tech University, Lubbock, USA.

Funding

Patient Genetic Determinants of Chronic Wound Microbiome CompositionR15GM141973 · NIGMS · TEXAS TECH UNIVERSITY · PI PHILLIPS, CALEB DAKOTAH · 2021 to 2021
$421k
NIGMS NIH HHS R15 GM141973
6 · The paper itself

Abstract

Wound etiology, host characteristics, and the wound microbiome contribute to chronic wound development. Yet, there is little accounting for the relative importance of these factors to predict wound healing. Here, a structural equation model was developed to provide such an explanatory and predictive framework. Chronic wounds from 565 patients treated at a clinic practicing biofilm-based wound care were included. Patient information included DNA sequencing-based wound microbiome clinical reports corresponding to initial clinical visit. Wound microbiome data was integrated into the SEM as a latent variable using a pre-modeling parcel optimization routine presented herein for the first time (available as R library

Indexed as

Chronic Woundsitem parcelingMicrobiomePredictive ModelingSEMStructural Equation Modeling

Identifiers

PMID39974037
PMCPMC11838970

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.