ArticleJSES international2025
Analyzing practice pattern in treating partial-thickness rotator cuff tears: a dual perspective from national database and American Shoulder and Elbow Surgeons PARCIAL research group.
Article in JSES international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Partial-thickness rotator cuff tears (PTRCTs), often considered less severe than full-thickness rotator cuff tears (FTRCTs), can be equally symptomatic. Despite the prevalence of PTRCTs, scholarly attention has predominantly focused on FTRCTs, resulting in a relative neglect of PTRCTs. This study aimed to assess the incidence of surgical repairs for PTRCTs in the United States (U.S.) using the Nationwide Ambulatory Surgery Sample (NASS) database and delineate practice patterns among a group of American Shoulder and Elbow Surgeons (ASES) members. Methods: The NASS database, representing approximately 67% of U.S. ambulatory surgical encounters, was queried to obtain the incidence of PTRCT and FTRCT repairs for the year 2019. Data on patient sex, hospital location, setting, teaching affiliation and size, and primary payor were also obtained. Separately, a retrospective chart review was conducted by 13 ASES research group members, collecting data on rotator cuff repairs performed from July 2021 to June 2022 at their individual institutions. Collected data included age, sex, extent and type of rotator cuff tears, duration of symptoms prior to surgical repair, and type and duration of nonoperative treatment. Results: The NASS database showed that PTRCT repairs accounted for at least 23% of the 187,787 rotator cuff repairs performed in 2019, with potential underestimation due to unspecified tear categorization. The retrospective chart review of the ASES research group revealed that PTRCT repairs constituted 27.8% of all rotator cuff repairs with substantial variability among institutions (8.6%-67.1%), that nonoperative measures were commonly employed, with a mean 12-month duration before surgical repair, and that the most common type of nonoperative treatment was supervised physical therapy combined with corticosteroid injection (37.7%). Discussion: This study revealed approximately one-fourth of all rotator cuff repairs conducted in the U.S. were attributed to PTRCTs with substantial variability in proportion of PTRCT repairs across individual orthopedic practices. The overall duration of nonoperative treatment was consistent with the existing literature. This study provides insights into the landscape of PTRCT repairs and associated practice patterns. Further investigation into factors influencing treatment decisions is warranted.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.