Quantifying the Occurrence of Shoulder Pain after Cardiac Surgery in a Cardiac Rehabilitation Population
Bibliographic record
Abstract
Purpose: The aim of this study was to identify the occurrence of shoulder pain after cardiac surgery among cardiac rehabilitation participants (CRPs) and its interaction with cardiac rehabilitation (CR). Method: This was a cross-sectional questionnaire-based study of open-heart surgery patients conducted at the midpoint of a 6-month CR programme. We measured the proportion of patients experiencing shoulder pain, onset, location, impact on rehabilitation, and pain and disability using the Shoulder Pain and Disability Index. Results: Of 70 (76% men) CRPs, 47% (33) reported shoulder pain post-surgery, with most (91%; 29 of 32) remaining symptomatic at the time of questionnaire completion, 14.6 (SD 37.9) months post-surgery. Disability and pain scores were 4.2 (SD 2.8) and 5.7 (SD 2.5), respectively (maximum score 10). Of people with shoulder pain participating in resistance training (RT; 19), 8 (42%) reported it was beneficial for shoulder pain; 7 (37%), no effect or unknown; and 4 (21%), some aggravation. Modifications to RT by programme staff were reported by 47% (8) of participants. Of those with shoulder pain, 10 (31%) reported some benefit; 20 (63%), no effect or unknown; and 2 (6%), aggravation from aerobic training. Conclusions: Almost half of the CRPs who had undergone open-heart surgery reported moderately severe and disabling shoulder pain that persisted for at least 14.6 (SD 37.9) months. Almost half the RT participants were prescribed exercise modifications with few negative effects. Shoulder pain is a significant issue after surgery, and appropriate screening is recommended for safe CR participation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".