Predicting pain and function outcomes in people consulting with shoulder pain: the PANDA-S clinical cohort and qualitative study protocol
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Abstract
People presenting with shoulder pain considered to be of musculoskeletal origin is common in primary care but diagnosing the cause of the pain is contentious, leading to uncertainty in management. To inform optimal primary care for patients with shoulder pain, the study aims to (1) to investigate the short-term and long-term outcomes (overall prognosis) of shoulder pain, (2) estimate costs of care, (3) develop a prognostic model for predicting individuals’ level and risk of pain and disability at 6 months and (4) investigate experiences and opinions of patients and healthcare professionals regarding diagnosis, prognosis and management of shoulder pain.
Methods and analysis
The Prognostic And Diagnostic Assessment of the Shoulder (PANDA-S) study is a longitudinal clinical cohort with linked qualitative study. At least 400 people presenting to general practice and physiotherapy services in the UK will be recruited. Participants will complete questionnaires at baseline, 3, 6, 12, 24 and 36 months. Short-term data will be collected weekly between baseline and 12 weeks via Short Message Serevice (SMS) text or software application. Participants will be offered clinical (physiotherapist) and ultrasound (sonographer) assessments at baseline. Qualitative interviews with ≈15 dyads of patients and their healthcare professional (general practitioner or physiotherapist).
Short-term and long-term trajectories of Shoulder Pain and Disability Index (using SPADI) will be described, using latent class growth analysis. Health economic analysis will estimate direct costs of care and indirect costs related to work absence and productivity losses. Multivariable regression analysis will be used to develop a prognostic model predicting future levels of pain and disability at 6 months using penalisation methods to adjust for overfitting. The added predictive value of prespecified physical examination tests and ultrasound findings will be examined. For the qualitative interviews an inductive, exploratory framework will be adopted using thematic analysis to investigate decision making, perspectives of patients and clinicians on the importance of diagnostic and prognostic information when negotiating treatment and referral options.
Ethics and dissemination
The PANDA-S study has ethical approval from Yorkshire and The Humber-Sheffield Research Ethics Committee, UK (18/YH/0346, IRAS Number: 242750). Results will be disseminated through peer-reviewed publications, social and mainstream media, professional conferences, and the patient and public involvement and engagement group supporting this study, and through newsletters, leaflets and posters in participating sites.
Trial registration number
ISRCTN46948079 .
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19198865.
Short Summary of Main Findings This 2021 medRxiv preprint (v1) is a study protocol — not a report of results. It describes the design of the PANDA-S (Prognostic And Diagnostic Assessment of the Shoulder) study: a prospective longitudinal cohort of patients consulting primary care with a new episode of shoulder pain, linked with a nested qualitative study. The main aims are to: (1) describe short- and long-term pain and function outcomes (prognosis), (2) estimate healthcare costs, and (3) develop and validate a clinical prediction model for pain and function outcomes to support stratified care. No empirical findings or results are presented, as data collection had not yet occurred at the …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19198865.
Short Summary of Main Findings This 2021 medRxiv preprint (v1) is a study protocol — not a report of results. It describes the design of the PANDA-S (Prognostic And Diagnostic Assessment of the Shoulder) study: a prospective longitudinal cohort of patients consulting primary care with a new episode of shoulder pain, linked with a nested qualitative study. The main aims are to: (1) describe short- and long-term pain and function outcomes (prognosis), (2) estimate healthcare costs, and (3) develop and validate a clinical prediction model for pain and function outcomes to support stratified care. No empirical findings or results are presented, as data collection had not yet occurred at the time of posting.
How This Work Has Moved the Field Forward It outlines a large, well-designed UK primary-care cohort study (with ISRCTN registration) that addresses key gaps in shoulder pain research: poor understanding of prognosis in real-world primary care settings, high healthcare costs, and the lack of validated tools for predicting individual outcomes. By planning to develop a prognostic model and incorporate patient/clinician perspectives via qualitative work, it lays the foundation for future stratified or personalised care approaches in shoulder management, moving beyond one-size-fits-all treatment.
Major Issues
This is purely a protocol paper with no results, outcomes, or data analysis (common for preprints of this type).
No peer-reviewed results from the completed cohort have been identified in major searches (as of 2026); only the protocol and related qualitative papers are published.
Potential for high loss to follow-up in a long-term primary-care cohort, which could affect model development (though not yet testable).
Protocol published in BMJ Open (2021); the medRxiv version is the preprint of that protocol.
Minor Issues
As a protocol, it contains no actual findings, so "main findings" section is inherently empty.
Title is long and descriptive but clear.
Limited detail in some protocol sections on exact statistical methods for model validation (planned but not executed).
Competing interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they did not use generative AI to come up with new ideas for their review.
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