The use of tranexamic acid in patients submitted to primary total hip and knee arthroplasty: a single-center retrospective observational study
Bibliographic record
Abstract
Introduction: This study aimed to determine the efficacy of intravenous tranexamic acid (TXA) in change in hemoglobin levels and need of red blood cell (RCB) transfusion following total hip arthroplasty (THA) and total knee arthroplasty (TNA) when compared with non-tranexamic acid treatment. Material and methods: We retrospectively reviewed 33 hips and 44 knees between January 2023 and December 2023. Outcome measures were hemoglobin drop (among postoperative first day), need of RBC transfusion, number of RBC units, hemoglobin levels among preoperative, and postoperative 1, 2, and 5 day. Results: Among THA patients, use of TXA compared to the non-TXA group was associated with a reduced need for RBC transfusion (12.5% vs. 32.0%; p = 0.496). Drop in hemoglobin levels within the first postoperative day was significantly greater in the non-TXA group (2.4 ± 1.12) compared to the TXA group (1.48 ± 0.53; p = 0.032). Among TKA patients, drop in hemoglobin levels on the first postoperative day was significantly smaller in the TXA group (1.05) than in the non-TXA group (2.31), with a significant p-value of < 0.001, with the need of RBC transfusion at 20% vs. 83.3%, respectively (p < 0.001). Conclusion: Our study supports the use of TXA as an effective measure to reduce the need for RBC transfusion in patients undergoing THA and TKA. The findings suggest that TXA may contribute to better postoperative outcomes by maintaining higher hemoglobin levels and reducing the need for transfusion.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".