Hospitalized Infections in People With Osteoarthritis: A National US Study
Bibliographic record
Abstract
OBJECTIVE: To study the incidence, time trends, and outcomes of serious infections in people with osteoarthritis (OA). METHODS: We used 1998-2016 US National Inpatient Sample (NIS) data. Using recommended weights, we examined the epidemiology of 5 types of serious infections requiring hospitalization in people with OA (opportunistic infections [OIs], skin and soft tissue infections [SSTIs], urinary tract infections [UTIs], pneumonia, and sepsis/bacteremia). We performed multivariable-adjusted logistic regression analyses to analyze factors associated with healthcare utilization (hospital charges, length of hospital stay, discharge to nonhome setting), and in-hospital mortality. RESULTS: Of all serious infection hospitalizations, 46,708,154 were without OA and 3,258,416 had OA. People with OA were 16.4 years older, more likely to be female (52% vs 65%), White (59% vs 70%), have a Deyo-Charlson Comorbidity Index (DCCI) ≥ 2 (42% vs 51%), receive Medicare (54% vs 80%), and less likely to receive care at an urban teaching hospital (45% vs 39%). Serious infection rates per 100,000 NIS hospitalizations increased from the study period of 1998-2000 to 2015-2016: OI (from 4.5 to 7.2); SSTI (from 48.4 to 145.9); UTI (from 8.4 to 104.6); pneumonia (from 164.0 to 224.3); and sepsis (from 39.4 to 436.3). In multivariable-adjusted analyses, older age, higher DCCI, sepsis, northeast region, urban hospital, and medium or large hospital bed size were significantly associated with higher healthcare utilization outcomes and in-hospital mortality; Medicaid insurance, non-White race, and female sex were significantly associated with higher healthcare utilization. CONCLUSION: Serious infection rates have increased in people with OA. Association of demographic, clinic, and hospital variables with serious infection outcomes identifies potential targets for future interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".