Etiology, Outcomes, and Complications of Total Hip Arthroplasty in Younger Patients: A Nationwide Big Data Analysis
Bibliographic record
Abstract
Background: This study investigates the rising trend of total hip arthroplasty (THA) in patients under 55 years old, commonly referred to as “younger” THA patients. Traditionally a procedure for older adults with osteoarthritis, THA is increasingly performed on younger patients. Methods: Using data from the Nationwide Inpatient Sample (NIS) from 2016 to 2019, we analyze the factors driving this trend, including the causes of hip issues, patient characteristics, and coexisting medical conditions. The study examines in-hospital mortality, length of stay, post-surgical complications, and hospitalization costs for 231,630 THA patients aged 18–54.9, identified using ICD-10 codes. Results: Statistical analysis revealed that younger patients (aged 18–34.9) had higher rates of chronic anemia, inflammatory bowel disease, sickle cell disorders, connective tissue disorders, and coagulation defects compared to patients aged 35–44.9 and 45–54.9. They also experienced the longest hospital stays (2.08 days) and highest costs ($70,540). Significant odds ratios were found for sickle cell disorders (36.078), coagulation defects (1.566), inflammatory bowel disease (2.582), connective tissue disorders (11.727), hip dislocation (3.447), and blood transfusion (1.488) in younger patients compared to other THA patients. Conclusions: Comprehensive analysis of these unique needs is crucial for optimizing care, tailoring treatment, managing co-existing conditions, and personalizing recovery strategies to improve outcomes and quality of life for younger THA patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".