Total Hip Arthroplasty in 6690 Patients with Inflammatory Arthritis: Effect of Medical Comorbidities and Age on Early Mortality
Bibliographic record
Abstract
OBJECTIVE: We analyzed early mortality after total hip arthroplasty (THA) in patients with inflammatory arthritis (IA), adjusting for medical comorbidities and socioeconomic background. METHODS: Data on 6690 patients with IA who underwent THA during 1992-2012 were extracted from the Swedish Hip Arthroplasty Register. Data on comorbidity, measured using the Charlson Comorbidity Index (CCI), and socioeconomic data were gathered from the Swedish National Inpatient Register and Statistics Sweden. The CCI was divided into low (0), moderate (1-2), and high (> 2). Cox proportional hazards models were fitted to calculate adjusted HR of early mortality, with 95% CI. RESULTS: Twenty-five patients (0.4%) died within 0-90 days, giving a 90-day unadjusted survival rate of 99.6% (CI 99.5-99.8). Comorbidity was associated with an increased risk of death within 90 days postoperatively [high vs low CCI: adjusted HR 9.0 (CI 1.6-49.9)]. There was a trend toward lower risk of death during the period 1999-2005, although patients operated on during this period had more comorbidities than those operated on from 1992 to 1998. A large proportion of patients was re-admitted to hospital within 90 days after the index procedure (30.2%), but rarely for cardiovascular reasons. CONCLUSION: Medical comorbidity and an age above 75 years are associated with a substantial increase in the risk of early death after THA in patients with IA. Awareness of potential risk factors may alert clinicians and thus improve perioperative care.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".