Clinical outcomes of patients lost to follow-up and factors affecting follow-up loss after total knee arthroplasty
Bibliographic record
Abstract
Abstract Background The hypotheses were as follows: 1) the clinical outcome of patients lost to follow-up after total knee arthroplasty (TKA) will be different compared to patients with follow-up; 2) follow-up rate will be affected by various social economic factors. Methods Patients who underwent TKA between March 2019 and February 2020 were retrospectively included. Patients lost to follow-up were defined as patients who did not undergo follow-up 6 months after TKA; all patients were divided into follow-up and follow-up loss groups. Western Ontario and McMaster Universities Osteoarthritis (WOMAC) and Knee Society Score (KSS) were measured before surgery. After surgery, WOMAC, KSS function, and satisfaction were measured via telephone. Age, sex, unilateral or bilateral TKA, distance from hospital, presence of a family, and insurance were investigated. Results A total of 137 patients were included in the study. There were 92 (67.2%) patients that followed up 6 months after TKA, on the other hand, 45 patients (32.8%) were lost to follow-up. There was no difference in clinical outcomes (WOMAC, p = 0.932; KSS clinical, p = 0.450) and satisfaction (pain: p = 0.230, function: p = 0.300) between two groups. Age, sex, unilateral or bilateral TKA, distance from hospital, presence of a family, and insurance had no effect on follow-up rates. Conclusion The clinical outcomes of patients lost to follow-up after TKA did not show a difference from those who were followed up. Age, sex, unilateral or bilateral TKA, distance from hospital, presence of a family, insurance status, and postoperative clinical symptoms did not affect the follow-up rate.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".