Epidemiological factors associated with revision of total joint replacement surgery: A nested case-control study
Bibliographic record
Abstract
Abstract Background: Total Joint Replacement (TJR) is the most effective treatment for patients with end-stage joint pathologies, such as osteoarthritis (OA). However, this procedure can result in prosthesis failure, consequently requiring an early revision. These revision surgeries come at a high cost to the healthcare system and increase morbidity to patients. Therefore, it is crucial to identify factors associated with revisions to inform surgical decision-making. Methods: This study was a nested-case control study utilizing participants recruited in the he Newfoundland Osteoarthritis Study (NFOAS), initiated in 2011. Study participants were patients who underwent TJR (hip/knee) due to various pathologies, with OA accounting for a large proportion of cases. Revision status was collected through a chart review on the Eastern Health Meditech Health Care Information System. Seventy-two variables collected by general health questionnaires and medical reports were examined for associations with revisions. Results: A total of 1086 patients were recruited in the study; 810 patients were included (41.5% hip and 58.5% knee) in the final analysis; 30 of them underwent revision surgery. Seven factors were identified to be associated with revision status (number of live births, hysterectomy, hypertension, lateral epicondylitis, back pain that radiates to legs, more than five comorbidities, and the surgeon; all p<0.05). Of these variables, patients requiring revisions were more likely to have had lateral epicondylitis or their primary surgery conducted by the surgeon coded E5. In contrast, non-revision patients were associated with more live births, more than five comorbidities, hysterectomy, hypertension, or back pain that radiated to their legs.Conclusions: Our data suggested that surgeon and patients' health factors were associated with the odds of requiring revision surgery. Therefore, patients' risk of revision surgery could theoretically be calculated prior to TJR, better informing patient treatment decisions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".