The efficacy of preoperative tranexamic acid administration among patients undergoing arthroscopic rotator cuff repair: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Aim To conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) that investigated the effectiveness of tranexamic acid (TXA) among patients undergoing arthroscopic rotator cuff repair (ARCR). Methods Five databases were screened until December 18, 2022. The included RCTs were assessed for risk of bias, and the endpoints were summarized as mean difference/standardized mean difference (MD/SMD) or risk ratio (RR) with the 95% confidence interval (CI) in a random-effects model. Results Seven RCTs with 510 patients (TXA = 261 and control/placebo = 249) were analyzed. The overall risk of bias was “low” and “unclear” in four and three RCTs, respectively. The mean operative time (n = 5 RCTs, MD = −9.64 min, 95% CI [−15.74, −3.54], p = 0.002) and mean postoperative pain score on postoperative day 1 (n = 5 RCTs, MD = −0.56, 95% CI [−1.06, −0.05], p = 0.03) were significantly reduced in the TXA group compared with the control group. However, there were no significant differences between both groups regarding visual clarity, amount of irrigation solution, and estimated intraoperative blood loss. Conclusion Among patients undergoing ARCR, preoperative TXA did not reduce intraoperative blood loss or improve visual clarity. However, TXA administration correlated with significant reductions (statistically) in operative time and postoperative day 1 pain score compared with the control group. Level of evidence: Level I; Systematic Review and Meta-analysis of Randomized Controlled Trials
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".