Evidence map›Paper›PMID 38529252›Full record

ReviewAnnals of joint2023

Massive rotator cuff tears: algorithmic approach to surgical treatment.

Kaitlyn Guadagno, Uma Srikumaran, Eric G Huish, Matthew J Best

Open access · diamondAbstract readReview
In one paragraph

Review in Annals of joint, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

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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 2 institutions in 1 country.

Kaitlyn GuadagnoDepartment of Orthopaedic Surgery, San Joaquin General Hospital, French Camp, CA, USA.
Uma SrikumaranDepartment of Orthopaedic Surgery, Johns Hopkins Hospital, Baltimore, MD, USA.
Eric G HuishDepartment of Orthopaedic Surgery, San Joaquin General Hospital, French Camp, CA, USA.
Matthew J BestDepartment of Orthopaedic Surgery, Johns Hopkins Hospital, Baltimore, MD, USA.
Johns Hopkins Hospital · USSan Joaquin General Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The management of massive rotator cuff tears (MRCT) presents a unique challenge to many orthopedic specialists. Unlike tears that are predicted to do well with primary, complete repair, MRCT are affected by tissue retraction, in-elasticity, bursal scarring, muscle atrophy, and fatty degeneration; operative repair thus portends worse healing rates than smaller tears and is associated with recurrent tear rates of up to 91% based on ultrasonography and magnetic resonance imaging (MRI). Rotator cuff tears are a common condition in patients over the age of 50. Thus, multiple advances in treatment strategies have been made to combat the limited efficacy of complete or partial rotator cuff repair in the setting of a massive or irreparable rotator cuff tears. It is of utmost importance that the operating orthopedic surgeon be familiar with these various treatment modalities to best serve the patient and that they harbor these skills within their armamentarium. This article details a review of the current literature including nonoperative and operative treatments for the management of massive and irreparable rotator cuff tears. The primary objective is to propose a literature-based algorithm for the treatment of massive and often irreparable rotator cuff tears to allow for informed ease in the decision-making process.

Indexed as

irreparablereverse total shoulder arthroplasty (RSA)rotator cuff repairRotator cuff teartendon transfer

Identifiers

PMID38529252
PMCPMC10929299
OpenAlexW4387783691

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.