Tranexamic acid for reducing blood loss following vaginal delivery: a double-blind randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Postpartum haemorrhage (PPH) is a major cause of maternal morbidity and mortality worldwide. Tranexamic acid (TXA) is a useful drug for prevention of PPH and merits evaluation in Nigeria, where PPH is the leading cause of maternal death (25%) and severe maternal morbidity. This study evaluates the efficacy of TXA in reducing blood loss following vaginal delivery. METHODS: This was a double-blind randomized placebo-controlled study on the efficacy and safety of intravenous TXA in reducing blood loss in women undergoing vaginal delivery in a tertiary hospital. Data analysis was conducted with IBM SPSS software (version 20, Chicago II, USA). P-value < 0.05 was considered statistically significant. RESULTS: The mean estimated blood loss was lower in TXA compared with the placebo group. (174.87 ± 119.83 ml versus 341.07 ± 67.97 ml respectively; P < 0.0001). PPH (blood loss > 500 ml) was 5.13% in the study arm compared to the control arm 7.14%- risk ratio (RR) 0.71; 95% CI: 0.38-1.79, p = 0.5956]. Additional uterotonics was required more in the control group compared to the treatment group 14(16.67%) versus 3(3.85%), p-value= 0.007. There were no major complications noticed in the treatment group. CONCLUSION: This study demonstrated that intravenous administration of TXA reduced blood loss following vaginal delivery. It also reduced the need for additional uterotonics. However, blood loss greater than 500 was not significantly reduced. TRIAL REGISTRATION: This trial was registered retrospectively. Pan African Clinical Trial Registry: PACTR202010828881019 on 12/10/2020.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".