Do Recent Trends in RA Surgery Reflect Success in Disease Management?
Bibliographic record
Abstract
Longterm outcomes are critically important for patients living with a chronic disease such as rheumatoid arthritis (RA). Because joints are replaced for endstage damage to the target organ in RA, studies that identify changes in the use of arthroplasty provide insight into changes in longterm RA outcomes. In this issue of The Journal , Young and colleagues1 studied arthroplasties to evaluate use trends for patients with RA. The investigators used data from the National Inpatient Sample (NIS), the largest US all-payer inpatient database, which contains a sample of over 1000 hospitals in the United States participating in the Healthcare Cost and Utilization Project. They assessed trends in the proportion of patients with RA among arthroplasty patients undergoing total shoulder (TSA), elbow (TEA), knee (TKA), hip (THA), and ankle (TAA) arthroplasty between 2002 and 2012. They determined the incidence of arthroplasties based on the US population, and compared the data from the first year of the study, 2002, to the final year, 2012. While studies using large databases such as the NIS have strengths and weaknesses, the large numbers contained in this database permit recognition of surgical trends and can be used to assess trends in less frequently performed procedures such as TAA or TEA. However, relevant data such as disease severity, presence of erosions, or medication use are not available, and coding errors or misclassification may occur. The investigators note, however, that because misclassification is unlikely to change over time, the effect on … Address correspondence to Dr. S.M. Goodman, 535 East 70th St., New York, New York 10021, USA. E-mail: Goodmans{at}hss.edu
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".