Evidence map›Paper›PMID 28616348›Full record

ReviewJournal of thoracic disease2017

Predictive risk models for proximal aortic surgery.

Daniel Hernandez-Vaquero, Rocío Díaz, Isaac Pascual, Rubén Álvarez, Alberto Alperi, Jose Rozado, Carlos Morales, Jacobo Silva, César Morís

Abstract readReview
In one paragraph

Review in Journal of thoracic disease, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Reoperations on Thoracic Aorta and Aortic Root: Surgical Technique and Pitfalls.The International journal of angiology : official publication of the International College of Angiology, Inc · 2018
    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

9 authors.

Daniel Hernandez-VaqueroHeart Area, Central University Hospital of Asturias, Oviedo, Spain.
Rocío DíazHeart Area, Central University Hospital of Asturias, Oviedo, Spain.
Isaac PascualHeart Area, Central University Hospital of Asturias, Oviedo, Spain.
Rubén ÁlvarezHeart Area, Central University Hospital of Asturias, Oviedo, Spain.
Alberto AlperiHeart Area, Central University Hospital of Asturias, Oviedo, Spain.
Jose RozadoHeart Area, Central University Hospital of Asturias, Oviedo, Spain.
Carlos MoralesHeart Area, Central University Hospital of Asturias, Oviedo, Spain.
Jacobo SilvaHeart Area, Central University Hospital of Asturias, Oviedo, Spain.
César MorísHeart Area, Central University Hospital of Asturias, Oviedo, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predictive risk models help improve decision making, information to our patients and quality control comparing results between surgeons and between institutions. The use of these models promotes competitiveness and led to increasingly better results. All these virtues are of utmost importance when the surgical operation entails high-risk. Although proximal aortic surgery is less frequent than other cardiac surgery operations, this procedure itself is more challenging and technically demanding than other common cardiac surgery techniques. The aim of this study is to review the current status of predictive risk models for patients who undergo proximal aortic surgery, which means aortic root replacement, supracoronary ascending aortic replacement or aortic arch surgery.

Indexed as

aortic aneurysmDecision support modelsthoracicthoracic surgery

Identifiers

PMID28616348
PMCPMC5462717

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.