A systematic review of clinical outcomes for outpatient vs. inpatient shoulder arthroplasty
Bibliographic record
Abstract
BackgroundOutpatient shoulder arthroplasty is growing in popularity as a cost-effective and potentially equally safe alternative to inpatient arthroplasty. The aim of this study was to investigate literature relating to outpatient shoulder arthroplasty, looking at clinical outcomes, complications, readmission, and cost compared to inpatient arthroplasty.MethodsWe conducted a systematic review of Medline, Embase and Cochrane Library databases from inception to 6 April 2020. Methodological quality was assessed using MINORS and GRADE criteria.ResultsWe included 17 studies, with 11 included in meta-analyses and 6 in narrative review. A meta-analysis of hospital readmissions demonstrated no statistically significant difference between outpatient and inpatient cohorts (OR = 0.89, p = 0.49). Pooled post-operative complications identified decreased complications in those undergoing outpatient surgery (OR = 0.70, p = 0.02). Considerable cost saving of between $3614 and $53,202 (19.7–69.9%) per patient were present in the outpatient setting. Overall study quality was low and presented a serious risk of bias.DiscussionShoulder arthroplasty in the outpatient setting appears to be as safe as shoulder arthroplasty in the inpatient setting, with a significant reduction in cost. However, this is based on low quality evidence and high risk of bias suggests further research is needed to substantiate these findings.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".