Shoulder Arthroplasty in Patients with Inflammatory Arthritis: Preoperative and Perioperative Management of Disease Modifying Anti-Rheumatic Drug Therapy
Bibliographic record
Abstract
Introduction Inflammatory arthritis is a debilitating systemic autoimmune and inflammatory disease that leads to joint damage, resulting in significant pain and disability. Rheumatoid arthritis (RA) is the most common inflammatory arthritis typically associated with advanced arthritic changes of the glenohumeral joint as well as with rotator cuff tears.20 Since the introduction of disease modifying anti-rheumatic drug (DMARD) therapy, patients diagnosed with inflammatory rheumatic diseases have observed improvements in pain management and functional outcomes, alongside a reduction in the occurrence of upper limb arthroplasties.16 Nonetheless, total joint arthroplasty still remains common in the treatment of RA.8,14 One recognized challenge in shoulder arthroplasty in the context of inflammatory arthritis is the perioperative management of anti-inflammatory medications. Approximately 75-84% of patients undergoing arthroplasty take traditional DMARDs or biologics.14 Management of these medications currently varies across rheumatology organizations. For instance, the American College of Rheumatology recommends withholding tumor necrosis factor (TNF)-α inhibitors for more than a week prior to surgery, British Society recommends withholding for 3-5 times the half-life of the drug, and Canadian Rheumatology Association propose withholding for 2 half-lives of the drug.14,22,31 Understanding the appropriate timing for discontinuing or continuing these medications is a critical element of perioperative management in shoulder arthroplasty, as it involves balancing the potential risks of post-operative disease flares with concerns for poor wound healing and infection.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".