Evidence map›Paper›PMID 26059560›Full record

ReviewEuropean journal of trauma and emergency surgery : official publication of the European Trauma Society2016

Bedside ultrasound procedures: musculoskeletal and non-musculoskeletal.

Lydia Sahlani, Laura Thompson, Amar Vira, Ashish R Panchal

Abstract readReview
PubMed Publisher
In one paragraph

Review in European journal of trauma and emergency surgery : official publication of the European Trauma Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Observational
  11. Article
  12. Educational impact of hand motion analysis in the evaluation of FAST examination skills.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2020
    Article
  13. 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.

Lydia SahlaniDepartment of Emergency Medicine, The Ohio State University Wexner Medical Center, Columbus, USA. Lydia.Sahlani@osumc.edu.ORCID http://orcid.org/0000-0002-6954-225X
Laura ThompsonDepartment of Emergency Medicine, The Ohio State University Wexner Medical Center, Columbus, USA.
Amar ViraDepartment of Emergency Medicine, The Ohio State University Wexner Medical Center, Columbus, USA.
Ashish R PanchalDepartment of Emergency Medicine, The Ohio State University Wexner Medical Center, Columbus, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The widespread availability of ultrasound (US) technology has increased its use for point of care applications in many health care settings. Focused (point of care) US is defined as the act of bringing US evaluation to the bedside for real-time performance. These images are collected immediately by the practitioner, allowing for direct integration into the physician's medical decision-making process. The real-time bedside diagnostic ability of US becomes a key tool for the management of patients. The purpose of this review is to (1) provide a general description of the use of focused US for bedside procedures; (2) specify the indications and common techniques used in bedside US procedures; and (3) describe the techniques used for each bedside intervention.

Indexed as

UltrasonographyWounds and InjuriesClinical Decision-MakingEarly Medical InterventionHumansMusculoskeletal DiseasesPoint-of-Care SystemsUltrasonicsPoint of care ultrasoundUltrasoundUltrasound procedures

Identifiers

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.