Prevalence of Persistent Pain of the Neuropathic Subtype after Total Hip or Knee Arthroplasty
Bibliographic record
Abstract
Purpose: This study aimed to (1) estimate the point prevalence of persistent postoperative pain (PPP) identified using the Self-Administered Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) after unilateral primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) using data from a registry of total joint arthroplasty (TJA) patients in Ontario, (2) estimate the effect of PPP on function, (3) estimate the prevalence of neuropathic pain (NP) features among patients with persistent pain, (4) determine participant characteristics in order to estimate the potential predictors of NP classification among individuals with persistent pain after TJA, (5) estimate the extent to which the estimates of prevalence depended on the measure used (i.e., S-LANSS vs. NP sub-scale of the Short-Form McGill Pain Questionnaire 2 [NP-SF-MPQ-2]), and (6) determine the difference in characteristics between those with and without NP. Method: This was a prospective follow-up study of a historical cohort of individuals who had undergone primary unilateral THA or TKA. Persistent pain was operationally defined as pain rated as 3 or more (out of 5) on the Oxford Pain Questionnaire 6 months or 1 year after THA or TKA. Participants with persistent pain completed the S-LANSS and the NP-SF-MPQ-2. Results: A total of 1,143 participants were identified as having had a TJA, 148 (13%) of whom had PPP. A total of 67 recipients completed the S-LANSS and the NP-SF-MPQ-2. Of these, an NP subtype was identified among 19 (28%; those with an S-LANSS score ≥ 12) to 29 (43%; those with an NP-SF-MPQ-2 score ≥ 0.91). Individuals with persistent pain of the NP subtype after TJA reported severe pain intensity and higher disability levels 1.5–3.5 years after surgery compared with those without persistent pain. Conclusions: A significant proportion of patients have persistent pain post-unilateral THA or TKA.
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 | 0.003 |
| 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) | 0.001 | 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".