Chronic pain after primary total and medial unicompartmental knee arthroplasty for osteoarthritis: a Danish nationwide cross-sectional survey
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Contemporary data on the risk of chronic pain after total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) is limited. Therefore, we aimed to investigate the incidence of chronic pain, pain characteristics, patterns of analgesic use, patient satisfaction, and willingness to undergo the same surgery again, 1 year after primary TKA and UKA for osteoarthritis. METHODS: We conducted a nationwide online survey among unselected patients who underwent primary TKA or medial UKA for primary osteoarthritis in Denmark. At 1 year postoperatively, we assessed the incidence of moderate to severe pain (≥ 4 on the 0-10 numerical rating scale), frequency of pain, pain interference with everyday life, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain domain, the Douleur Neuropathique 4 interview (DN4i), use of analgesics, satisfaction, and willingness to undergo the same surgery again. RESULTS: We sent survey invitations to 2,580 TKA patients and 1,007 UKA patients who underwent surgery in 2022. Of the 70% TKA respondents, 25% had moderate to severe chronic pain, 82% were satisfied/very satisfied with the result of surgery, and 86% indicated that they would choose to undergo surgery again. Of the 75% UKA respondents, 23% had moderate to severe chronic pain, 86% were satisfied/very satisfied, and 88% would undergo the same surgery again. CONCLUSION: In Denmark, 25% of TKA patients and 23% of medial UKA patients experienced moderate to severe knee pain after 1 year. These numbers were higher than most previous estimates. Most patients were satisfied with the result of surgery and would undergo the same surgery again.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".