Application of Tranexamic Acid in Total Knee Arthroplasty – Prospective Randomized Trial
Bibliographic record
Abstract
BACKGROUND: The use of tranexamic acid (TXA) in total knee arthroplasty (TKA) has shown good results. Bleeding may cause local complications consequently greater pain and reduced function postoperatively. No study has related the use of TXA to these facts. OBJECTIVE: The aim was to evaluate the effects of TXA haemoglobin, Western Ontario and McMaster Universities Index (WOMAC), pain intensity and flexion gain after TKA. METHODS: 43 patients were randomized and then underwent TKA. TXA was applied to 22 of these patients before closure of the joint capsule. Haemoglobin measurements (mg/dL) were taken preoperatively and 24 and 48 hours after surgery. The WOMAC questionnaire and pain visual analogue scale (VAS) were applied, and flexion gain was measured up to the second postoperative month. Statistical analysis compared the results to determine whether there were differences between the groups for each of the evaluated times. RESULTS: There were differences in favour of the drug 48 hours postoperatively for the haemoglobin variable (p = 0.01), in pain evaluation, 24 and 48 hours, postoperatively (p < 0.01) and in flexion gain, 24 hours after surgery (p = 0.03). There were no significant differences between the groups in the haemoglobin evaluation 24 hours postoperatively, in pain assessment 7 days, 21 days and 2 months, postoperatively, in flexion gain 48 hours, 7 days, 21 days and 2 months, postoperatively and in WOMAC after 2 months. CONCLUSION: In addition to reducing bleeding, topical TXA improved pain and increased flexion gain in the first hours after TKA. TRIAL REGISTRATION: RBR-9b4qgq.
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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) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".