Risk Factors for Readmissions After Total Joint Replacement
Bibliographic record
Abstract
»: We performed a systematic review and meta-analysis of predictive modeling studies examining the risk of readmission after total hip arthroplasty (THA) and total knee arthroplasty (TKA) in order to synthesize key risk factors and evaluate their pooled effects. Our analysis entailed 15 compliant studies for qualitative review and 17 compliant studies for quantitative meta-analysis. »: A qualitative review of 15 predictive modeling studies highlighted 5 key risk factors for risk of readmission after THA and/or TKA: age, length of stay, readmission reduction policy, use of peripheral nerve block, and type of joint replacement procedure. »: A meta-analysis of 17 studies unveiled 3 significant risk factors: discharge to a skilled nursing facility rather than to home (approximately 61% higher risk), surgery at a low- or medium-procedure-volume hospital (approximately 26% higher risk), and the presence of patient obesity (approximately 34% higher risk). We demonstrated clinically meaningful relationships between these factors and moderator variables of procedure type, source of data used for model-building, and the proportion of male patients in the cohort. »: We found that many studies did not adhere to gold-standard criteria for reporting and study construction based on the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) and NOS (Newcastle-Ottawa Scale) methodologies. »: We recommend that these risk factors be considered in clinical practice and future work alike as they relate to surgical, discharge, and care decision-making. Future work should also prioritize greater observance of gold-standard reporting criteria for predictive models.
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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 | 0.028 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".