Neuropathic-like symptoms have an additional predictive value for chronic postsurgical pain in total hip and knee arthroplasty patients
Bibliographic record
Abstract
BACKGROUND: A significant portion of total knee and hip arthroplasty (TKA/THA) patients experience chronic postsurgical pain (CPSP). The prevalence of afflicted individuals ranges from 10 to 34%. CPSP is the main cause of postoperative dissatisfaction. For prevention purposes it is essential to know which preoperative factors are predictive for CPSP. It is unknown whether neuropathic-like symptoms add predictive value to known predictors for CPSP and dissatisfaction after TKA/THA. METHODS: A prospective cohort study including 453 TKA/THA patients (TKA 208, THA 245) was conducted. Pain intensity (numeric rating scale [NRS]) and neuropathic-like symptoms (modified-painDETECT questionnaire [mPDQ]; score ≥ 13) were obtained preoperatively. One year postoperatively, CPSP and dissatisfaction (single NRS item (0-10); dissatisfied: ≤ 5) were captured: CPSP by means of the Oxford Knee/Hip Score (moderate or severe pain on question 1) as well by pain intensity at rest and with movement (NRS ≥ 1). Multivariate logistic regression modeling was used to determine the additive predictive value of preoperative neuropathic-like symptoms (mPDQ ≥ 13) on experiencing CPSP and dissatisfaction for the total group and for knee and hip patients separately. RESULTS: Preoperative neuropathic-like symptoms (m-PDQ ≥ 13) had an additional value for experiencing CPSP after one year, with odds ratios (p < 0.05) ranging from 2.16 (total group) to 4.15 (hip patients). Neuropathic-like symptoms had no additional value for predicting CPSP in knee patients or for predicting dissatisfaction. CONCLUSION: The results of this study showed that neuropathic-like symptoms (m-PDQ ≥ 13) have an additional predictive value over known predictors, especially in hip patients. Patients with neuropathic-like symptoms have over twice the odds of suffering from CPSP one year after TKA/THA. Neuropathic-like symptoms had no additional value for predicting dissatisfaction.
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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 | 0.001 | 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".