Risks and trends of red blood cell transfusion in obstetric patients: a retrospective study of 45,213 deliveries using administrative data
Bibliographic record
Abstract
BACKGROUND: Transfusion data for obstetric patients are scarce. Identifying characteristics associated with red blood cell transfusion (RBCT) is of importance to better identify patients who would benefit from blood conservation strategies as the risk of alloimmunization from RBCT has the potential to affect the fetus and newborn. STUDY DESIGN AND METHODS: We conducted a retrospective cohort study using hospital administrative data to identify trends and risk factors of RBCT in obstetric patients. Data were analyzed according to the mode of delivery. RESULTS: A total of 45,213 deliveries were captured between January 1, 2007, and December 31, 2013. A higher proportion of patients undergoing cesarean sections (C/Ss) received an RBCT (2.3%) compared to other modes of delivery (0.7% for spontaneous vaginal delivery, 1.5% for instrumental delivery; p < 0.001). In addition, the risk of RBCT increased over the 7-year period for those patients undergoing C/S (relative risk [RR], 1.56; 95% confidence interval [CI], 1.14-2.15). An unavailable hemoglobin (Hb) level (RR, 12.94; 95% CI, 7.39-22.66) and Hb level of 70 to 80 g/L (RR, 7.78; 95% CI = 5.21-11.60) were strongly associated with RBCT among women undergoing C/S. Earlier gestational age at induction increased the risk of RBCT across all modes of delivery. CONCLUSIONS: The higher frequency of RBCT for unknown and low Hb supports the need for predelivery patient blood management at the time of delivery. The additional risk factors associated with RBCT identified may be used to develop risk stratification tools by mode of delivery to assist in the identification of patients at the highest risk of requiring RBCT.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".