Effectiveness of Perineural Injections Combined with Standard Postoperative Total Knee Arthroplasty Protocols in the Management of Chronic Postsurgical Pain After Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND Despite increased experience and technical developments in total knee arthroplasty (TKA), chronic postsurgical pain (CPSP) remains one of physicians' biggest challenges. The aim of the present study was to evaluate the effectiveness of perineural injection therapy (PIT) in the management of CPSP after TKA. MATERIAL AND METHODS A total of 60 patients who had been surgically treated with TKA because of advanced knee osteoarthritis was included in the present study. The study included 2 groups. Group A consisted of patients who received 3 rounds of PIT combined with standard postoperative TKA protocol during the same period. Group B received standard postoperative TKA protocols (rehabilitation programs, oral and intravenous analgesics). Clinical effectiveness was evaluated via Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Visual Analog Scale (VAS) at baseline and 1-, 3-, and 6-month follow-ups. RESULTS All repeated measures showed significant improvements (P<0.001) in both groups for VAS and WOMAC scores. These scores were significantly better in group A in all follow-up periods compared with group B (P<0.001). Twenty-nine patients (93.5%) in group A reported excellent or good outcomes compared with 26 patients (89.6%) in group B. CONCLUSIONS PIT is a promising approach in CPSP with minimal cost, simple and secure injection procedures, minimal side effects, and higher clinical efficacy.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".