Evidence map›Paper›PMID 36595065›Full record

SynthesisSurgical endoscopy2023

Prediction models in hernia repair: a systematic review.

Zachary Malaussena, Karthik Pittala, Abdul-Rahman F Diab, Salvatore Docimo

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Surgical endoscopy, 2023. 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
0.3field-weighted citation impact, top 45% 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

4 authors at 1 institution in 1 country.

Zachary MalaussenaUniversity of South Florida Morsani College of Medicine, 560 Channelside Dr, Tampa, FL, 33602, USA. zmalaussena@yahoo.com.ORCID 0000-0002-9127-4730
Karthik PittalaUniversity of South Florida Morsani College of Medicine, 560 Channelside Dr, Tampa, FL, 33602, USA.
Abdul-Rahman F DiabDepartment of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
Salvatore DocimoUniversity of South Florida Morsani College of Medicine, 560 Channelside Dr, Tampa, FL, 33602, USA.
University of South Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHernias can present with unique challenges when it comes to management and repair. Prediction models can be a useful tool for clinicians to better anticipate and understand the severity of a hernia, the type of surgical technique, or presurgical planning that may be required to treat the patient, and the risk of complications. Our goal is to evaluate and consolidate prediction models in hernia repair present in the literature for which physicians can reference to best improve patient outcomes and postoperative management.

methodsWe performed a literature search in PubMed using keywords, "rectus width to defect width ratio," "predicting myofascial release," "computed tomography hernia repair prediction," "component separation radiology prediction hernia," "fat volume and hernia repair," "body morphometrics and Query hernia repair," "body morphometrics and reherniation," "computed tomography findings and risk of emergency hernia repair," "loss of domain and hernia radiology," and "volumetry and hernia repair." We searched for publications that used radiographic parameters to predict hernia severity, interventions, and outcomes.

resultsUsing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we found twenty-three studies related to prediction models in hernia repair published between 2000 and 2021. We summarized studies pertaining to predicting acute care, predicting operative planning with loss of domain and component separation, predicting complications, paraesophageal hernia predictions, and predicting postoperative respiratory complications.

conclusionRadiographic prediction models can be an objective and efficient way for surgeons to analyze hernias and better understand a patient's situation so that they can inform patients about the best treatment options and the risk of complications.

Indexed as

Hernia, HiatalHernia, VentralHerniorrhaphyHumansPostoperative ComplicationsRecurrenceSurgical MeshTomography, X-Ray ComputedComplex hernia repairHernia and radiologyHernia radiologyPredictive models and hernia repairRadiology and emergent hernia repairRadiology and loss of domainRadiology predictive models

Identifiers

PMID36595065
OpenAlexW4313453308

What OpenQuestion holds

Textmetadata
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.