The Effect of Disease Severity and Comorbidity on Length of Stay for Orthopedic Surgery in Rheumatoid Arthritis: Results from 2 UK Inception Cohorts, 1986–2012
Bibliographic record
Abstract
OBJECTIVE: To examine factors predicting length of stay (LoS) for orthopedic intervention in rheumatoid arthritis (RA). METHODS: LoS for orthopedic intervention was examined in 2 consecutive, multicenter inception cohorts: the Early RA Study (n = 1465, 9 centers) and the Early RA Network (n = 1236, 23 centers). Date, type of orthopedic procedure, and LoS were recorded and validated against national data, the UK National Joint Registry, and the UK Hospital Episode Statistics database. Clinical, laboratory, and radiographic measures and comorbidity recorded at baseline and annually were examined for their predictive power on LoS using regression analysis. RESULTS: A total of 770 of 2701 patients (28.5%) had 1602 orthopedic interventions: 40% major (mainly total hip/knee replacements), 24% intermediate (mainly hand/wrist and ankle/foot surgery), and 16% minor (mainly soft tissue surgery). Median (interquartile range) LoS was 8 (5-13), 3 (1-5), and 1 (0-2) days for major, intermediate, and minor interventions, respectively. Older age predicted longer LoS (p < 0.001) whereas a more recent operation year predicted shorter LoS (p < 0.001). Markers of active disease, namely low hemoglobin, high Health Assessment Questionnaire, and high Disease Activity Scores in the first year all predicted longer LoS for all types of surgery (p = 0.001, p < 0.001, p = 0.05, respectively). Presence of 1 or more major comorbidities predicted longer LoS (p < 0.001). CONCLUSION: Comorbidity and standard clinical and laboratory markers of disease activity affect the LoS for orthopedic surgery in RA, which has important clinical and economic implications, providing a target for improving patient outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".