Can radiological findings detect neuropathic pain in patients with osteoarthritis of the knee undergoing surgery?
Bibliographic record
Abstract
OBJECTIVES: The mechanism underlying neuropathic pain (NP) in osteoarthritis (OA) of the knee is not completely understood. This study aimed to investigate whether possible NP in patients with knee OA undergoing knee surgery is associated with specific radiological findings. METHODS: This study included 197 patients who underwent knee surgery for symptomatic knee OA. Clinical evaluation was performed using the Central Sensitization Inventory (CSI), Western Ontario and McMaster Universities Arthritis Index (WOMAC), and PainDETECT questionnaire. Radiological evaluation was performed using the hip-knee-ankle (HKA) angle, posterior tibial slope (PTS), varus and valgus laxities, and magnetic resonance imaging OA Knee Score (MOAKS). Radiological findings were compared between patients with possible and unlikely NP. Logistic regression analysis was performed to identify the predictive factors for NP. RESULTS: There were 163 and 34 patients with unlikely NP and with possible NP, respectively. The percentage of patients with CSI score ≥ 40 was significantly higher in the possible NP group than in the unlikely NP group (17.6% vs. 6.1%). Patients with possible NP had worse WOMAC scores than patients with unlikely NP. There were no significant positive associations between the possible NP and radiological findings in knee OA. Regression analysis showed no predictive factors for possible NP. CONCLUSIONS: Possible NP is not associated with specific radiological findings in knee OA. Patients with possible NP may mediate CS and experience more severe symptoms, including decreased knee function and lower quality of life, than patients with unlikely NP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".