Analgesic Control and Functional Outcome After Knee Arthroscopy
Bibliographic record
Abstract
OBJECTIVE: Hyaluronic acid (HA) is a naturally occurring substance within normal synovial joints. Although its efficacy in treating osteoarthritis has been evaluated, it has not been established whether it is of benefit after routine arthroscopic procedures. We hypothesized that immediate supplementation with HA after completion of arthroscopy would result in improved short-term analgesic and functional outcomes after knee arthroscopy. DESIGN: Double-blinded randomized controlled trial. SETTING: Tertiary referral center. PATIENTS: One hundred ten patients presenting for routine arthroscopic procedures were invited to participate in the study. After exclusion criteria were applied, 98 patients were randomized to receive either 10 mL of 0.5% bupivacaine or 3 mL of HA into the joint immediately after completion of surgery. INTERVENTIONS: After completion of surgery, all patients were randomized to receive either 10 mL of 0.5% bupivacaine or 3 mL of HA into the knee joint. MAIN OUTCOME MEASURES: Visual analogue scale (VAS) pain scores were obtained at baseline; 1, 2, and 24 hours; and 1, 2, and 6 weeks after surgery. Western Ontario and McMaster Universities (WOMAC) and Tegner-Lysholm scores were obtained at baseline and then at 1, 2, and 6 weeks after surgery. RESULTS: Forty-nine patients received intra-articular bupivacaine and 49 received HA. There was no statistical difference in any of the outcome measures (VAS pain scores, WOMAC, and Tegner-Lysholm) at any time point between the groups overall. CONCLUSIONS: There was no benefit of HA injection immediately at the end of knee arthroscopy in the first 6 weeks after surgery. CLINICAL RELEVANCE: Routine use of HA at the time of knee arthroscopy cannot be recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".