Patient Positioning in Arthroscopic Management of Posterior‐Inferior Shoulder Instability: A Systematic Review Comparing Beach Chair and Lateral Decubitus Approaches
Bibliographic record
Abstract
PURPOSE: To analyze the available literature pertaining to clinical outcomes and complications of posterior-inferior shoulder stabilization performed arthroscopically in either the beach chair (BC) or lateral decubitus (LD) position. METHODS: According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), 3 databases (PubMed, EMBASE, and Medline) were searched up to January 2018 for English-language studies on posterior shoulder instability. Descriptive statistics are presented. The Methodological Index for Non-Randomized Studies (MINORS) scale was used to assess quality. RESULTS: Twenty-five studies were included, examining 1,085 patients (n = 140 BC; n = 945 LD), of mean age 25.0 years, 27.1% female, and mean 3.1 years of follow-up. MINORS scores for BC and LD were 11.2 and 9.8, respectively. Regardless of positioning, patients did not differ across numerous outcomes and various surgical factors (e.g., number of portals, anchors, anchor types, concomitant pathology, or postoperative rehabilitation protocol). Postoperative patient satisfaction ranged from 85% to 87.5% and 93% to 100% for patients treated in BC and LD positions, respectively. Although not reported for BC, overall and preinjury return-to-play (RTP) rates in LD patients ranged from 72% to 100% and 55% to 100%, respectively, returning from 3 to 7.6 months postoperatively. Failure rates in the BC and LD positions ranged from 0% to 9.4% and 0% to 29%, respectively. There were no differences in reported incidences of neuropraxia, stroke, nonfatal pulmonary embolus, vision loss, cardiac arrest, or other positioning-related complications. CONCLUSIONS: Arthroscopic management of posterior-inferior shoulder instability has a successful track record and minimal complication profile. Although patient positioning appears to influence results, with those treated in the LD position experiencing marginally higher patient satisfaction and failure rates, the current data prevent any conclusions being made regarding the superiority of one approach over another. As the clinical relevance of patient positioning remains to be determined, larger, higher-level study designs with long-term follow-up are required. LEVEL OF EVIDENCE: Level IV, systematic review of Level II, III, and IV studies.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".