Evidence map›Paper›PMID 39640542›Full record

ArticleOrthopedic reviews2024

Understanding Orthopaedic Infections: A Conceptual Approach.

Gregory Laborde, Christopher Bloise, George Karam

Abstract read
In one paragraph

Article in Orthopedic reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

3 authors.

Gregory LabordeSchool of Medicine Louisiana State University Health Sciences Center New Orleans, Louisiana.
Christopher BloiseDepartment of Orthopaedic Surgery Louisiana State University Health Sciences Center New Orleans, Louisiana.
George KaramDepartment of Internal Medicine Louisiana State University Health Sciences Center New Orleans, Louisiana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Memorizing mounds of facts without having an effective conceptual framework to store them is often an unsuccessful learning style because memory frequently fails without understanding. To accomplish this goal, we offer a framework of ideas called "concept maps". Of the many strategies for metacognition (that is, thinking about thinking), concept mapping is proven to improve meaningful and independent learning. We used this approach to guide understanding of common orthopaedic infections. This paper is not meant to serve as an approach on how to treat, but rather as a storage system with the ability to assist in understanding orthopaedic infections. Methods: Concept maps are constructed using information from over 30 sources of orthopaedic or infectious disease literature. The sequential levels of each concept map are referred to as levels of hierarchy. Boxes of each level of hierarchy are designed to be a point of differentiation between a similar concept. Branches form lower levels of hierarchy to further divide specific concepts. Results: 5 concept maps were constructed. One map is an overview of all concepts, and the key concept for each of the following map are as follows: osteomyelitis, synovial infection, myositis, and foreign material infection. Conclusion: A conceptual approach is a way of learning material in a manner that allows effective storage and retrieval. This approach will ultimately lead to a better understanding of orthopaedic infections.

Indexed as

foreign material infectionmyositisorthopaedic infectionsosteomyelitissynovitis

Identifiers

PMID39640542
PMCPMC11617197

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.