Aspirin is as effective as low molecular weight heparins in preventing symptomatic venous thromboembolism following arthroscopic anterior cruciate ligament reconstruction
Bibliographic record
Abstract
Abstract Objective Little evidence exists on the optimal agent for thromboprophylaxis following arthroscopic anterior cruciate ligament reconstruction (ACLR). This study was conducted to compare the effectiveness of aspirin and low molecular weight heparins (LMWHs) to prevent symptomatic venous thromboembolism (VTE) following arthroscopic ACLR and their safety of use. Methods In this retrospective study, we investigated patients who underwent ACLR surgery between March 2016 and March 2021 based on inclusion and exclusion criteria. The rate of venous thromboembolism events and wound complications were statistically compared between the patients that received an LMWH and those who took aspirin for thromboprophylaxis. We also used logistic regression modeling to assess the effect of the prophylactic agent on the likelihood of developing VTE. Result 761 patients (761 knees) were included. 458 and 303 patients had received aspirin and LMWH, respectively. There was no significant difference in the demographic factors of the two groups. Five patients in the aspirin group (1.09%) and five patients in the LMWHs group (1.65%) developed a symptomatic VTE event (P value = 0.530). The two groups were not significantly different in terms of other complications, such as hemarthrosis or surgical site infection (P > 0.05). Conclusion Aspirin is as effective as LMWH in preventing VTE events after ACL reconstruction. Prospective studies including a no-prophylaxis group are warranted to assess this issue further. Level of Evidence: III
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".