What are the predictors of response to physiotherapy in patients with massive irreparable rotator cuff tears? Gaining expert consensus using an international e-Delphi study
Bibliographic record
Abstract
BACKGROUND: Massive irreparable rotator cuff tears can cause significant shoulder pain and disability. Treatment options include physiotherapy or surgery, with a lack of research comparing treatment options. For physiotherapy there is uncertainty about which patients will have a successful or unsuccessful response to treatment and a lack of consensus on what constitutes the best physiotherapy programme. With these significant gaps in the research, it is challenging for clinicians seeing patients with massive irreparable rotator cuff tears to advise on what is their best treatment pathway. METHODS: A three round Delphi study was conducted with expert shoulder physiotherapists and orthopaedic surgeons to gain consensus on the important factors associated with response to physiotherapy in this patient population. Round 1 was an information-gathering round to identify predictors of response to physiotherapy in patients with massive irreparable rotator cuff tears. Rounds 2 and 3 were consensus-seeking rounds on the importance and modifiability of the predictors. Consensus criteria were determined a priori using median, interquartile range, percentage agreement and Kendall's Coefficient of Concordance. RESULTS: Participants were recruited April-October, 2023. 88 experts participated in Round 1 and of these, 70 completed Round 3 (79.54%). In Round 1, content analysis of 344 statements identified 45 predictors. In Round 2, 29 predictors reached consensus as important and 2 additional predictors were identified. In Round 3, of the 31 predictors from Round 2, 22 reached consensus as important and 12 of these reached consensus as modifiable by physiotherapists. Both patient factors and clinician factors from a broad range of domains reached consensus: biomechanical, psychological, social, co-morbidities, communication / healthcare interactions and pain. CONCLUSIONS: The results of this Delphi study suggest that clinicians assessing patients with massive irreparable rotator cuff tears should assess across all these domains and target the modifiable factors with interventions. Particular emphasis should be placed on optimising modifiable clinician factors including therapeutic alliance, comprehensive explanation of the condition and collaborative and realistic goal-setting. These in turn may influence modifiable patient factors including patient expectations, engagement with the physiotherapy programme, motivation and self-efficacy thus creating the ideal environment to intervene on a biomechanical level with exercises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".