Maternal and neonatal predictive variables for quality of cord blood as a source of stem cells
Bibliographic record
Abstract
Objective: This study aimed to identify the maternal and neonatal predictive variables for quality of cord blood as a source of stem cells.Methods: A descriptive design was followed in this study. A purposive sample of 143 pairs; pregnant women assigned for elective cesarean section and their newborns, was enrolled in current study. The subjects were recruited from the Operating Theater of Mansoura University Hospital, Egypt. One hundred forty three umbilical cord blood units were collected in utero from cesarean deliveries. The collected units were assessed for its blood volume and total nucleated cells content. An assessment sheet of 12 items was used as a tool of data collection.Results: The greater blood volume and higher nucleated cells obtained from the primigravida, between 37 to 40 gestation weeks, and those not exposed to cigarette smoke during pregnancy (163.1 ± 24.8 ml and 10.2 ± 2.8 × 106 cells/ml, 169.3 ± 22.6 ml and 11.2 ± 1.9 × 106 cells/ml, 169.8 ± 21.4 ml and 10.0 ± 2.9 × 106 cells/ml respectively). The birth weight ≥ 3,600 g, the first born baby, and the heavier placenta (≥ 550 g) produced significantly greater blood volume and cellular content (161.5 ± 20.6 ml and 9.8 ± 2.4 × 106 cells/ml, 164.6 ± 19.4 ml and 9.6 ±2.7 cells/ml, 164.8 ± 19.6 ml and 10.2 ± 2.6 × 106 cells/ml respectively).Conclusions: Primigravida, delivery between 37 to 40 gestation weeks, and no exposure to cigarette smoke during pregnancy were the maternal variables that were associated with larger blood volume and higher cellular content. Even though, first born baby, birth weight ≥ 3,600 g, and placenta weight ≥ 550 g were the neonatal variables.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".