Trends in prevalence and implant types in the Nova Scotia Joint Database Registry between 2005 and 2021
Bibliographic record
Abstract
Aims: The aim of this study is to evaluate the change in incidence rate of shoulder arthroplasty, indications, and surgeon volume trends associated with these procedures between January 2003 and April 2021 in the province of Nova Scotia, Canada. Methods: A total of 1,545 patients between 2005 and 2021 were analyzed. Patients operated on between 2003 and 2004 were excluded due to a lack of electronic records. Overall, 84.1% of the surgeries (n = 1,299) were performed by two fellowship-trained upper limb surgeons, with the remainder performed by one of the 14 orthopaedic surgeons working in the province. Results: Total shoulder arthroplasty (TSA) was the most frequent procedure (32.17%; n = 497), followed by stemmed hemiarthroplasty (SHA) (27.7%; n = 428). The most frequent indication for primary shoulder arthroplasty was degenerative osteoarthritis (58.1%; n = 882), followed by acute proximal humerus fracture in 15.11% (n = 245), and rotator cuff arthropathy in 14.18% (n = 220). The overall rate of revision was 7.7% (2.8% to 11.2%). The number of TSAs and reverse shoulder arthroplasties (RSAs) has been increasing since 2016. The amount of revision cases is proportional to the number of operations performed in the same year throughout the study period. Conclusion: The incidence of shoulder arthroplasty in the Maritime Provinces has increased over the last 16 years. Revision rates are similar the those found in other large database registries. Reverse shoulder arthroplasty prevalence has increased since 2016.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".