Arthroscopic Rotator Cuff Repair In Patients With Parkinson’s Disease: A Propensity Score Matching Study With Minimum 2-Year Follow-up
Bibliographic record
Abstract
BACKGROUND: Although the effectiveness of arthroscopic rotator cuff repair (ARCR) for structural and functional outcomes has been widespread proven, few researchers investigated the impact of ARCR on patients with Parkinson's Disease (PD), which may have previously been viewed as a relative contraindication to ARCR. METHODS: Data were collected retrospectively for all patients who underwent ARCR for small- to large-sized rotator cuff tears between September 2014 and May 2019. Patients were eligible for the study if they indicated that they diagnosed with rotator cuff repair and had minimum 2-year postoperative outcome scores for the range of motion (ROM), the Western Ontario Rotator Cuff Index (WORC), the Constant-Murley Score (CMS), the University of California, Los Angeles (UCLA), Pittsburgh Sleep Quality Index (PSQI), Hospital Anxiety and Depression Scale (HADS), and the visual analog scale (VAS) for pain. Propensity score matching (PSM), a statistical method, was used to screen a control group without PD matched 1:1 with similar age, sex, tear size, preoperative stiffness, and fatty infiltration, which have previously been identified as important factors influencing success rates. RESULTS: Three hundred and eighty-nine patients met all study criteria including required follow-up, of whom 31 and 358 with PD and without PD, respectively. After adjusting for confounders, the propensity score matched indicators were compared, patients with PD experienced significantly more pain (4.45 ± 2.43 vs. 0.52 ± 1.18; P<.001) and had lower WORC (49.10 ± 21.22 vs. 78.90 ± 17.54; P<.001), CMS (46.77 ± 22.24 vs. 79.45 ± 14.74; P<.001) and UCLA (21.11 ± 8.54 vs. 28.16 ± 6.16; P<.001) scores respectively than the matched control group. They also exhibited higher sleep disturbance (10.04 ± 5.36 vs. 5.19 ± 3.28; P<.001), as well as higher anxiety and depression psychological status at 24 months (P<.001; P<.001). Overall clinical outcomes from preoperatively to postoperatively were not improved significantly for patients with PD vs. without PD. CONCLUSION: Patients with PD experienced significantly more pain, resulted in worse shoulder functional outcomes, and reported persistently diminished mental and physical health status. Shoulder surgeons should be cognizant of PD as an outcome-modifying variable when treating patients with rotator cuff tears. This finding suggested that the need for ARCR in patients with PD should be carefully considered in the light of personalized needs and physical conditions.
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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 | 0.001 |
| 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".