Comparison of femoral triangle plus iPACK blocks with femoral triangle block alone for anterior cruciate ligament reconstruction: a randomized controlled clinical trial on postoperative pain and knee function
Bibliographic record
Abstract
BACKGROUND: Anterior cruciate ligament reconstruction (ACLR) can cause severe postoperative pain. However, consensus regarding the most effective regional analgesia is lacking. We hypothesized that, compared with femoral triangle block (FTB) and local infiltration analgesia, adding an iPACK block would decrease postoperative morphine consumption. METHODS: Patients scheduled for ACLR under general anesthesia were randomly allocated to the FTB (n=45) or the FTB+iPACK group (n=45). The primary outcome was the cumulative oral morphine equivalent (OME) consumption during the first two postoperative days. Secondary outcomes were maximum pain scores, opioid adverse effects, and knee functional scores (Knee Injury and Osteoarthritis Outcome (KOOS), International Knee Documentation Committee (IKDC) and Lysholm) 3, 6, and 9 months after surgery. RESULTS: Compared with FTB, FTB+iPACK resulted in similar OME consumption (median (IQR)=50 (14-103) vs 60 (32-89) mg, respectively; median of the difference (95% CI): 5 (-14, 28) mg, p=0.49). No significant intergroup differences were found in terms of pain scores, opioid-related side effects, or functional knee recovery. Pain and symptoms subscales of KOOS and IKDC at 9 months were higher for patients with an OME consumption <50 mg within the first two postoperative days, but these statistical differences did not reach the minimal clinically important difference. CONCLUSIONS: iPACK block has no additional analgesic benefits for primary ACLR in the setting of a multimodal analgesia regimen including FTB and local infiltration analgesia. TRIAL REGISTRATION NUMBER: NCT05136352.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".