How Systemic Sclerosis Affects Healthcare Use and Complication Rates after Total Hip Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To assess whether outcomes after primary total hip arthroplasty (THA) differ in systemic sclerosis (SSc). METHODS: We used the 1998-2014 US National Inpatient Sample. THA and SSc were identified using procedure and diagnostic codes, respectively. Multivariable-adjusted logistic regression analyses assessed the association of SSc with in-hospital complications (implant infection, revision, transfusion, mortality) post-THA and associated healthcare use (hospital charges, hospital stay, discharge to non-home setting), adjusting for age, sex, race, Deyo-Charlson comorbidity index, primary diagnosis for THA, household income, and insurance payer. RESULTS: Of the 4,116,485 primary THA performed in the United States in 1998-2014, SSc patients made up 0.06% (n = 2672). In multivariable-adjusted analyses, compared to people without SSc, people with SSc had higher adjusted OR (95% CI) of the following post-primary THA: (1) non-home discharge, 1.25 (95% CI 1.03-1.50); (2) hospital stay > 3 days, 1.61 (95% CI 1.35-1.92); (3) transfusion, 1.54 (95% CI 1.28-1.84); and (4) in-hospital revision, 9.53 (95% CI 6.75-13.46). Differences in in-hospital mortality had a nonsignificant trend [2.19 (95% CI 0.99-4.86)]. There were no differences in total hospital charges or implant infection rates. CONCLUSION: SSc was associated with a higher rate of in-hospital complications and healthcare use after primary THA. Future studies should examine whether pre- or postoperative interventions can reduce the risk of post-THA complications in people with SSc.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".