Factors Associated With Outcome Response Frequency in a Knee Replacement Registry
Bibliographic record
Abstract
INTRODUCTION: Nonresponse can potentially introduce bias in arthroplasty registries, compromising confidence in outcome data, diminishing both internal validity and generalizability. Patient-reported outcome measures can be critical tools in evaluating clinical outcomes after total knee arthroplasty; however, patient-reported outcome measure data can be skewed when subsets of the population are nonresponsive. This study investigates sociodemographic and clinical factors associated with 12-month nonresponse after total knee arthroplasty and the effects of comprehensive multimodal follow-up methods. METHODS: A prospective cohort of 2,508 total knee arthroplasty patients enrolled in the Function and Outcomes Research for Comparative Effectiveness registry between 2018 and 2023 was analyzed. Sociodemographic and clinical data were collected preoperatively, and comprehensive multimodal follow-up methods were implemented. Hierarchical cluster analysis identified characteristics associated with nonresponse, and logistic regression was used to validate these findings. RESULTS: At 12-month follow-up, 735 of 2,508 patients (29%; P < 0.0001) were nonresponsive. Nonresponders, represented by cluster 5, which had a 45.8% response rate, were more likely to be female (P < 0.0001), non-White or mixed race (P < 0.0001), Hispanic or Latino (P < 0.0001), have less than a college education (P < 0.0001), public insurance (P < 0.0001), greater comorbidity (P < 0.0001), and lower preoperative knee injury and osteoarthritis outcome scores (P < 0.0001). The highest response rate (76.9%) was found in cluster 1, which primarily included well-educated males (P < 0.0001), with private insurance (P < 0.0001), and a lower body mass index (P < 0.0001). CONCLUSION: (1) Persistent and multimodal follow-up methods, through e-mail, paper mailings, and phone calls are needed to achieve high response rates above international registry standards of 60%. (2) Identifying patient characteristics linked to nonresponse provides an opportunity to help with targeted response strategies. These strategies may help reduce selection bias, improve data collection through improved response rates, and enhance the long-term utility of arthroplasty registries.
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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 | 0.034 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".