Predicting Persistent Shoulder Pain in Primary Care
Author Information
Author(s): David Vergouw, Martijn W. Heymans, Henrica C.W. de Vet, Daniëlle A.W.M. van der Windt, Henriëtte E. van der Horst
Primary Institution: VU University Medical Center Amsterdam
Hypothesis
Can clinical consensus from a Delphi procedure predict persistent shoulder pain better than a statistical scoring system?
Conclusion
The study found that while both expert-based and statistical models identified important predictors of persistent shoulder pain, the statistical model performed better in predicting outcomes.
Supporting Evidence
- The expert panel identified symptom duration and pain catastrophizing as key predictors.
- The statistical model outperformed the expert-based models in predictive ability.
- Consensus was achieved among 97% of the panel members on the final selection of predictors.
Takeaway
Doctors tried to figure out what signs mean someone will have shoulder pain for a long time, and they found that using their experience wasn't as good as using a math model.
Methodology
A Delphi poll with three rounds of data collection was used to reach consensus among health care professionals on predictors for persistent shoulder pain.
Potential Biases
Potential bias due to the selection of predictors based on expert opinion rather than a comprehensive statistical analysis.
Limitations
The study relied on existing datasets which may not have included all variables suggested by the expert panel.
Participant Demographics
The expert panel consisted of 41 health care professionals from the UK and the Netherlands, including general practitioners and physiotherapists.
Statistical Information
P-Value
0.029
Confidence Interval
0.612 - 0.700
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website