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Predicting the outcome of conservative treatment with physiotherapy in adults with shoulder pain associated with partial-thickness rotator cuff tears – a prognostic model development study

Overview of attention for article published in BMC Musculoskeletal Disorders, September 2018
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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9 X users

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Title
Predicting the outcome of conservative treatment with physiotherapy in adults with shoulder pain associated with partial-thickness rotator cuff tears – a prognostic model development study
Published in
BMC Musculoskeletal Disorders, September 2018
DOI 10.1186/s12891-018-2239-8
Pubmed ID
Authors

Cordula Braun, Nigel C. Hanchard, Helen H. Handoll, Andreas Betthäuser

Abstract

Rotator cuff disorders represent the commonest type of painful shoulder complaints in clinical practice. Although conservative treatment including physiotherapy is generally recommended as first-line treatment, little is known about the precise treatment indications for subgroups of rotator cuff disorders, particularly people with shoulder pain associated with partial-thickness tears of the rotator cuff, PTTs: "symptomatic PPTs". The aim of this study was to develop a prognostic model for predicting the outcome of a phase of conservative treatment primarily with physiotherapy in adults with symptomatic PTTs. A prospective observational cohort study was conducted in an outpatient setting in Germany. Ten baseline factors were selected to evaluate nine pre-defined multivariable candidate prognostic models (each including between two and nine factors) in a cohort of adults with symptomatic atraumatic PTTs undergoing a three-month phase of conservative treatment primarily with physiotherapy. The primary outcome was change in the Western Ontario Rotator Cuff Index. The models were developed using linear regression and an information-theoretic analysis approach: Akaike's Information Criterion (AICC). Eight candidate models were analyzed using data from 61 participants. Two "best models" were identified: smoking & pain catastrophizing and disability & pain catastrophizing. However, none of the models had a satisfactory performance or precision. We could not determine a prognostic model with satisfactory performance and precision. Further high-quality prognostic model studies with larger samples are needed, but should be underpinned, and thus preceded, by robust research that enhances knowledge of relevant prognostic factors. DRKS00004462 . Registered 08 April 2014; retrospectively registered (prior to the analysis).

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X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 109 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 20 18%
Student > Master 19 17%
Student > Postgraduate 8 7%
Student > Ph. D. Student 8 7%
Researcher 5 5%
Other 16 15%
Unknown 33 30%
Readers by discipline Count As %
Nursing and Health Professions 32 29%
Medicine and Dentistry 25 23%
Sports and Recreations 3 3%
Agricultural and Biological Sciences 2 2%
Psychology 2 2%
Other 5 5%
Unknown 40 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2018.
All research outputs
#6,088,059
of 23,103,436 outputs
Outputs from BMC Musculoskeletal Disorders
#1,123
of 4,111 outputs
Outputs of similar age
#106,939
of 337,559 outputs
Outputs of similar age from BMC Musculoskeletal Disorders
#21
of 55 outputs
Altmetric has tracked 23,103,436 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 4,111 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 337,559 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.