Preoperative Autologous Blood Donation for Rh-Negative Pregnant Women Undergoing Cesarean Sections
Bibliographic record
Abstract
Background: Cesarean sections (C-section) often require blood transfusions in cases of severe bleeding, particularly challenging in Rh-negative pregnancies due to the scarcity of Rh-negative donors, with only approximately 0.3% of the population in Thailand. Autologous blood donation, where individuals donate their own blood before surgery, offers a promising solution. Our study focused on preparing preoperative autologous blood donations (PAD) for Rh-negative pregnancies. Methods: We conducted blood screening on 7,182 pregnancies at Takuapa Hospital from October 2013 to September 2018, identifying 21 Rh-negative pregnant women. We established criteria based on hemoglobin (Hb) levels, which are crucial for autologous blood preparation (Hb at 11.0 g/dL, and hematocrit (Hct) above 33%). Blood samples were collected twice during pregnancy, at 36 and 37 weeks, with the second collection 1 week before the C-section. Pregnancies testing positive for infectious markers were excluded following standard blood donation guidelines. Twelve pregnant women testing negative for infectious markers were enrolled. Results: The demographic data showed 12 subjects aged 17 to 41 years, with an average of 27.83. Initial blood tests indicated Hb and Hct levels of 12.5 g/dL, and 36.4%, slightly decreasing to 12.2 g/dL and 35.8% in the second collection. On the day of the cesarean, levels further declined to 11.6 g/dL and 34.4%, respectively, within normal ranges. At discharge, the Hct measured 34.8%. Maternal and infant health post-C-section were good, with baby weights ranging from 2,640 to 4,080 g. None of the 12 cases required autologous blood transfusion, validating the safety of standard autologous blood preparation practices. Conclusions: This study highlights the safety of autologous blood donation for pregnant women with rare blood types, which was achieved through effective planning and collaboration among hospital departments. These findings can serve as a model for other hospitals and significantly reduce the burden of searching for Rh-negative donors.
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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) | 0.000 | 0.000 |
| 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".