The impact of preexisting psychiatric disorders on patient outcomes following primary total shoulder arthroplasty: A systematic review and quantitative synthesis
Bibliographic record
Abstract
Purpose: To summarize complication rates, reoperation rates, length-of-stay (LOS), patient-reported outcome measures (PROMs), and range of motion following total shoulder arthroplasty (TSA) in patients with preexisting psychiatric disorders (PDs) compared to controls. Methods: Three databases (MEDLINE, PubMed, and EMBASE) were searched from inception to 4 March 2024 to identify studies comparing outcomes between patients undergoing anatomic (aTSA) or reverse TSA (rTSA) with or without a preexisting psychiatric condition. The authors adhered to the preferred reporting items for systematic reviews and meta-analyses and revised assessment of multiple systematic review guidelines. Data on demographics, as well as postoperative complication rates, reoperation rates, LOS, PROMs, and range of motion were extracted from included studies. PROMs included the American Shoulder and Elbow Surgeons (ASESs) score, and visual analogue scale (VAS) pain score. Meta-analyses were conducted for outcomes reported by multiple studies, with odds ratios (ORs) and mean differences (MDs) as effect measures for continuous and dichotomous outcomes, respectively. Results: Thirteen studies were included in this review, comprising a total of 820,831 TSA patients. The PD group (71.0% female) consisted of 150,432 patients (mean age: 67.6 ± 9.9) with a mean follow-up time of 34.1 ± 30.1 months. The control group (58.1% female) consisted of 670,399 patients (mean age: 69.4 ± 10.7) with a mean follow-up time of 39.1 ± 36.0 months. The PD group had significantly higher rates of complications and reoperation. The PD group also reported significantly lower postoperative ASES scores, higher postoperative VAS scores, and inferior postoperative abduction. There were no significant differences in postoperative LOS, forward flexion, internal rotation, or external rotation. Conclusion: Patients with preexisting PDs may have a one-and-a-half times higher odds of postoperative complication or reoperation, as well as significantly worse postoperative pain and PROMs. Identification of at-risk individuals with preexisting psychiatric conditions and preoperative referral to a mental health specialist to optimize psychiatric conditions may benefit this patient cohort ahead of their shoulder arthroplasty procedure. Level of evidence: IV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| 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 | 0.001 |
| 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".