Evidence map›Paper›PMID 41280720›Full record

ReviewShoulder & elbow2025

A historical review: Surgical management of massive irreparable rotator cuff tears.

Andrew Kailin Zhou, Dave Osinachukwu Duru, Saroop Nandra, Salma Chaudhury

Abstract readReview
In one paragraph

Review in Shoulder & elbow, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
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.

Andrew Kailin ZhouUniversity of Warwick, Warwick, UK.
Dave Osinachukwu DuruUniversity of Cambridge, Cambridge, UK.ORCID https://orcid.org/0009-0005-8396-6146
Saroop NandraUniversity of Warwick, Warwick, UK.
Salma ChaudhuryDepartment of Trauma and Orthopaedics, Addenbrookes Major Trauma Unit, Cambridge University Hospitals, Cambridge, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Massive irreparable rotator cuff tears (MIRCTs) represent a complex clinical challenge, resulting in pain, pseudoparalysis, and functional disability. Over the past century, their surgical management has undergone significant evolution, from early open repairs to sophisticated reconstructive and joint replacement strategies. Methods: A historical narrative review was conducted, informed by a systematic search of PubMed/MEDLINE, Embase, and landmark texts. Major surgical innovations-open repair, tendon transfers, debridement, superior capsular reconstruction (SCR), subacromial spacers, and reverse shoulder arthroplasty (RSA)-were examined in the context of their development and impact. Results: Open repairs offered limited success for chronic MIRCTs. Subacromial decompression and debridement provided symptom relief in low-demand patients. Tendon transfers and graft interpositions emerged as joint-preserving strategies in select cases. RSA transformed management by reliably restoring function in pseudoparalysis and cuff tear arthropathy. Contemporary techniques such as SCR and biologic augmentation aim to preserve native anatomy, but outcomes remain inconsistent. Discussion: MIRCT management has progressed from salvage repairs to a diverse toolkit of joint-preserving and reconstructive options. No single strategy is universally superior, underscoring the need for patient-specific approaches. Ongoing comparative trials and surgical consensus efforts are critical to optimising care.

Indexed as

Historicalmanagementmassive cuff tearsreview

Identifiers

PMID41280720
PMCPMC12634389

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.