Different Prevalence of Neuropathic Pain and Risk Factors in Patients with Knee Osteoarthritis at Stages of Outpatient, Awaiting and after Total Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Neuropathic pain (NP) plays an important role in patients with knee osteoarthritis (KOA). However, the prevalence of NP at different treatment stages including outpatient, awaiting and after total knee arthroplasty (TKA) have not been compared. The understanding of this issue and identify risk factors can help physicians develop individualized strategies to manage the pain of KOA. Therefore, the aim of the study is to investigate the prevalence and risk factors of NP at different treatment stages of KOA. METHODS: Patients diagnosed as KOA between August 2016 and August 2020 were enrolled in this cross-sectional study and divided into three groups according to treatment stage, including outpatient stage, awaiting TKA stage (pre-TKA) and after TKA stage (post-TKA). A numeric rating scale (NRS) and PainDETECT questionnaire were used to evaluate nociceptive pain and NP. Patient demographics, radiological assessments using Kellgren-Lawrence (K-L) grade, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were analyzed. Data analysis and statistics were processed using SPSS 20.0 and examined by ANOVA with/without Bonferroni correction or Kruskal-Wallis test. A chi-square test was used to determine cross-table data and calculate the odds ratio (OR) value. RESULTS: Of the 921 patients, the prevalence of possible and likely NP was 17.5% (56/320) and 2.5% (8/320) in the pre-TKA group compared with 3.4% (8/233) and 0.4% (1/233) in the outpatient group and 1.4% (5/368) and 0.5% (2/368) in the post-TKA group, respectively. In the pre-TKA group, higher NRS (NRS >3; OR = 10.65, 95% CI: 3.25-34.92, P < 0.001) and WOMAC pain score (score > 10; OR = 4.88, 95% CI: 2.38-10.01, P < 0.001) conferred an increased risk of unclear pain. Age, gender, BMI and K-L grade showed no significant differences among the unlikely, possible and likely NP groups. CONCLUSION: Prevalence of NP is different at stages of out-patient, awaiting and after TKA in patients with KOA. Patients awaiting TKA have the highest prevalence of NP compared with patients in outpatient and post-TKA groups. In the patients waiting for TKA, higher NRS (NRS >3) and WOMAC pain scores (score > 10) are risk factors of NP.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".