Complications of Shoulder Arthroplasty
Bibliographic record
Abstract
Update This article was updated on May 17, 2017, because of a previous error. On page 256, the sentence that had read “The current analysis revealed a total of 19,262 TSAs and RSAs at a mean follow-up of 40.3 months in 122 studies, with an overall complication rate of 7.4% (2,122 complications) 3-124 ” now reads “The current analysis revealed a total of 19,262 TSAs and RSAs at a mean follow-up of 40.3 months in 122 studies, with an overall complication rate of 11% (2,122 complications) 3-124 .” An erratum has been published: J Bone Joint Surg Am. 2017 June 21;99(12):e67. The most common complications after reverse shoulder arthroplasty in order of decreasing frequency included instability, periprosthetic fracture, infection, component loosening, neural injury, acromial and/or scapular spine fracture, hematoma, deltoid injury, rotator cuff tear, and venous thromboembolism (VTE). The most common complications after anatomic total shoulder arthroplasty (TSA) in order of decreasing frequency were component loosening, glenoid wear, instability, rotator cuff tear, periprosthetic fracture, neural injury, infection, hematoma, deltoid injury, and VTE. Glenoid component wear and loosening remain a common cause of failure after anatomic TSA, despite advances in surgical technique and implant design. Diagnostic confirmation of infection after shoulder arthroplasty remains a challenge. In the setting of a painful and stiff shoulder after arthroplasty, the surgeon should have a heightened suspicion for infection. Inflammatory markers may be normal, radiographs may be inconclusive, and prosthetic joint aspiration may be negative for a causative organism.
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 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.021 |
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".