Multidimensional Pain Assessment in Surgical Interventions of The Knee: ACL Reconstruction and Total Knee Arthroplasty
Bibliographic record
Abstract
This study investigated pain levels in patients undergoing anterior cruciate ligament (ACL) reconstruction and total knee arthroplasty (TKA), using a sample of 37 patients divided into two groups. Group G1 consisted of 20 elderly patients (mean age 71.59 years) diagnosed with unilateral knee osteoarthritis who underwent TKA with anesthetic block. Group G2 included 17 adults (mean age 31.62 years) with a complete ACL rupture who underwent ACL reconstruction surgery, also with anesthetic block. Pain was assessed at three time points (pre-surgery, 24 hours post-surgery, and 30 days post-surgery) using the Visual Analog Scale (VAS) and the McGill Pain Questionnaire. The results showed that in Group G1, pain measured by VAS decreased from a mean of 9.76 pre-surgery to 6.16 after 30 days. In Group G2, pain decreased from a mean of 3.76 to 0.71 over the same period. The Number of Words Chosen (NWC) index from the McGill questionnaire was significantly higher in Group G1 compared to Group G2 at all time points, with a statistically significant difference (p < 0.001). Additionally, the Pain Rating Index-Affective (PRI-A) was higher in Group G1 (mean of 10.0 pre-surgery) compared to Group G2 (mean of 3.54), showing a significant difference (p = 0.001). The study concludes that subjective pain assessments are crucial for guiding therapeutic interventions, particularly in surgical contexts involving anesthetic block.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".