Evidence map›Paper›PMID 42909187›Full record

ArticleArthroscopy, sports medicine, and rehabilitation2026

Artificial Intelligence Predicts Failure of Nonoperative Treatment in Degenerative Posterosuperior Rotator Cuff Tears.

Santiago Gabardo, Natalia Martínez-Catalán, José María Belmonte Martínez, Gregorio Bernabé García, José Manuel García Carrasco, Cristina Delgado, María Valencia-Mora, Gonzalo Luengo-Alonso, Emilio Calvo

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In one paragraph

Article in Arthroscopy, sports medicine, and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Santiago GabardoShoulder and Elbow Reconstructive Surgery Unit, Department of Orthopedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz Universidad Autónoma Madrid Spain.ORCID https://orcid.org/0000-0001-9242-8634
Natalia Martínez-CatalánShoulder and Elbow Reconstructive Surgery Unit, Department of Orthopedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz Universidad Autónoma Madrid Spain.ORCID https://orcid.org/0000-0001-7881-5873
José María Belmonte MartínezComputer Engineering Department University of Murcia Murcia Spain.ORCID https://orcid.org/0009-0004-9009-7324
Gregorio Bernabé GarcíaComputer Engineering Department University of Murcia Murcia Spain.ORCID https://orcid.org/0000-0002-7265-3508
José Manuel García CarrascoComputer Engineering Department University of Murcia Murcia Spain.
Cristina DelgadoShoulder and Elbow Reconstructive Surgery Unit, Department of Orthopedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz Universidad Autónoma Madrid Spain.ORCID https://orcid.org/0000-0003-2166-8264
María Valencia-MoraShoulder and Elbow Reconstructive Surgery Unit, Department of Orthopedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz Universidad Autónoma Madrid Spain.
Gonzalo Luengo-AlonsoShoulder and Elbow Reconstructive Surgery Unit, Department of Orthopedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz Universidad Autónoma Madrid Spain.ORCID https://orcid.org/0000-0003-3516-5043
Emilio CalvoShoulder and Elbow Reconstructive Surgery Unit, Department of Orthopedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz Universidad Autónoma Madrid Spain.ORCID https://orcid.org/0000-0002-6054-6078

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To develop an artificial intelligence-based algorithm to predict success of nonoperative treatment in degenerative posterosuperior tears and to identify the most influential features. Methods: Patients with degenerative rotator cuff tears initially treated nonoperatively were enrolled prospectively from January 2022 to November 2024. Exclusion criteria included age >75 years; <24 months of follow-up; massive, traumatic, and partial tears; arthritic changes (Hamada >2); and severe fatty degeneration (Goutallier >2). All patients followed a standardized physiotherapy protocol and could choose surgery if symptoms persisted; failure was defined as opting for surgery. Collected variables included demographics, comorbidities (diabetes, dyslipidemia, hypertension, hypothyroidism, and smoking), magnetic resonance imaging tear characteristics (size; International Society of Arthroscopy, Knee Surgery, and Orthopaedic Sports Medicine classification; associated cuff involvement; and biceps pathology), corticosteroid injection history, and functional scores (American Shoulder and Elbow Surgeons score, Subjective Shoulder Value, and the visual analog scale). First, a standard statistical analysis between groups was performed. Later, the dataset was split into a train (80%) and test (20%), and a random forest algorithm model was developed. Finally, SHapley Additive exPlanations values were computed to assess feature importance. Results: Of the 412 patients enrolled, 347 patients met the inclusion criteria. After 24 months, 66 (19%) patients opted for surgery. Mean follow-up was 30.4 months (range, 24-32). Compared with the successful nonoperative treatment group, patients who failed treatment were younger (58.5 vs 64.6 years, Conclusions: The artificial intelligence-based algorithm accurately predicted nonoperative treatment outcomes in degenerative posterosuperior rotator cuff tears (area under the curve 0.84, sensitivity 94%). Age, tear size, and function were the main predictors. Level of Evidence: Level IV, prognostic retrospective case series.

Identifiers

PMID42909187
PMCPMC13684103

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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.