Cost and Early Complication Analysis Following Total Hip Arthroplasty in Parkinson's Disease Patients: A Propensity-matched Database Study.
Bibliographic record
Abstract
Background: Parkinson's Disease is a well-known neuromuscular disorder, which affects the stability and gait of elderly patients. With the progressive increase in the life span of patients with PD, the problem of degenerative arthritis and the consequent need for total hip arthroplasty (THA) in this cohort are rising. There is paucity of data in the existing literature regarding the healthcare costs and overall outcome following THA in PD patients. The current study was planned to assess the hospital expenditure, details regarding hospital stay, and complication rates for patients with PD, who underwent THA. Methods: We investigated the National Inpatient Sample data to identify PD patients, who underwent hip arthroplasty from 2016 to 2019. Using propensity score, PD patients were matched 1:1 to patients without PD by age, gender, non-elective admission, tobacco use, diabetes, and obesity. Chi-square and T-tests were used for analyzing categorical and non-categorical variables, respectively (Fischer-Exact test was employed for values<5). Results: ). The in-hospital mortality was similar between the two groups. Conclusion: Patients with PD undergoing THA required greater proportion of emergent hospital admissions. Based on our study, the diagnosis of PD showed significant association with greater cost of care, longer hospital stay, and higher post-operative complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".