Do early patient reported outcome measures post total knee arthroplasty predict poor outcomes (the early PROMPT study)?
Bibliographic record
Abstract
INTRODUCTION: Approximately 20% of patients are not satisfied following Total knee arthroplasty (TKA). The ability to identify patients at risk of poor outcomes in the early-postoperative period following TKA could inform the development of targeted treatments with the ability to improve outcomes for these patients. This prospective cohort study aimed to identify early post-operative, prognostic factors for patients experiencing dissatisfaction and poor outcomes at 12-months post TKA. METHODS: Patients (n = 185) were recruited from TKA waiting lists at a single site, completed measures of pain intensity, neuropathic pain, anxiety, depression, generic and disease specific health related quality of life (HRQoL), satisfaction and expectations, at discharge from hospital; three weeks post-surgery and again at 1 year. ROC curve analysis identified which variables best predicted patients experiencing dissatisfaction (VAS < 5/10) and poor outcomes (Western Ontario and McMaster Universities Arthritis Index (WOMAC)<40) at 12-months. RESULTS: The strongest prognostic factors for dissatisfaction were low HRQoL (EQ5D3L) at both day of discharge (AUC = 0.812) and three weeks post-surgery (AUC = 0.810), and high pain levels (WOMAC pain sub-scale) at 3-weeks post-TKA (AUC = 0.796). The strongest prognostic factors for poor outcomes were poor function (WOMAC function sub-scale) at three weeks post TKA (AUC = 0.815); low HRQoL (EQ5D3L) at three weeks post-TKA (AUC = 0.783) and high levels of pain (WOMAC pain sub-scale) at 3 weeks post-TKA (AUC = 0782). Anxiety and depression at 3-weeks were also prognostic factors for dissatisfaction (AUC = 0.629 & AUC = 0.686) and poor outcomes (AUC = 0.632 & 0.713) at 12-months. CONCLUSION: This single site cohort study suggests that patients with low HRQoL, high pain levels, poor function, anxiety, and depression in the first three weeks following TKA are at risk of dissatisfaction and poor outcomes at one-year post-surgery.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".