Orthopedic Surgery in Rheumatoid Arthritis: Results from the Spanish National Registry of Hospitalized Patients over 17 Years
Bibliographic record
Abstract
OBJECTIVE: To analyze the trend of orthopedic surgery (OS) rates on patients with rheumatoid arthritis (RA). METHODS: Retrospective observational study based on information provided by the Spanish National System of Hospital Data Surveillance. All hospitalizations of patients with RA for orthopedic surgery [total hip arthroplasty (THA), total knee arthroplasty (TKA), arthrodesis, and upper limb arthroplasty (ULA)] during 1999-2015 were analyzed. The age-adjusted rate was calculated. Generalized linear models were used for trend analysis. RESULTS: There were 21,088 OS in patients over 20 years of age (77.9% women). OS rate adjusted by age was 754.63/100,000 RA patients/year (women 707.4, men 861.1). Neither an increasing nor a decreasing trend was noted for the total OS. However, trend and age interacted, so in the age ranges 20-40 years and 40-60 years, an annual reduction of 2.69% and 2.97%, respectively, was noted. In the age ranges over 80 years and 60-80 years, we noted an annual increase of 5.40% and 1.09%, respectively. The average age at time of OS increased 5.5 years during the period analyzed. For specific surgeries, a global annual reduction was noted in rates for arthrodesis. In THA, there was an annual reduction in patients under 80 years. In TKA and ULA, there was an annual reduction in patients under 60 years. CONCLUSION: Although the overall OS rate has not changed, there is a decrease in the rate of arthrodesis at all ages, THA in patients under 80 years of age, as well as TKA and ULA in patients under 60 years of age.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".