A nomogram for predicting total knee arthroplasty after arthroscopy in knee Osteoarthritis: The role of pain, inflammation, and sleep
Bibliographic record
Abstract
Knee osteoarthritis (OA) is a prevalent condition contributing significantly to public health burdens due to increased incidence from obesity and aging populations. Arthroscopy was a minimally invasive intervention for knee OA, yet its long-term efficacy in delaying total knee arthroplasty (TKA) remains uncertain. This study investigates factors predicting the progression of TKA post-arthroscopy. A retrospective cohort study was conducted involving patients with knee OA who underwent their first arthroscopy at a tertiary hospital. Patients were divided into two groups: those who received only arthroscopy and those who underwent subsequent TKA. Comprehensive demographic and clinical data, including inflammatory markers and baseline assessments, were collected. Multivariate logistic regression analysis was used to identify independent predictors of TKA necessity. A predictive nomogram was developed and internally validated. Out of 409 patients, 273 were treated with arthroscopy alone, while 136 underwent TKA. Significant predictors for TKA included higher postoperative pain (Visual Analogue Scale score, VAS score), elevated inflammatory markers (specifically high-sensitivity C-reactive protein, hs-CRP; interleukin-1 beta, IL-1β; and tumor necrosis factor alpha, TNF-α), longer disease duration, higher Pittsburgh Sleep Quality Index scores (PSQI scores), and lower baseline knee function (Hospital for Special Surgery score, HSS score). The nomogram demonstrated strong predictive accuracy, with a concordance index of 0.907. Patients in the TKA group exhibited significantly worse clinical indicators, such as higher Western Ontario and McMaster Universities Osteoarthritis Index scores (WOMAC scores) and PSQI scores, and increased serum inflammatory markers. The test set confirmed the model's validity, highlighting similar predictors for necessitating TKA post-arthroscopy (Area Under Curve = 0.887). A nomogram incorporating postoperative pain, specific inflammatory markers (hs-CRP, IL-1β, TNF-α), sleep quality, and functional scores demonstrated high accuracy in predicting the need for TKA following arthroscopy in knee OA patients, facilitating individualized risk assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".