Effect of leg wrapping technique on prevention of spinal induced hypotension and fetal acidosis during cesarean section: Randomized controlled trial
Bibliographic record
Abstract
Background and aim: Hypotension during cesarean section (CS) under spinal anesthesia has been a subject of scientific study for more than 50 years and the search for the most effective strategy to achieve hemodynamic stability remains challenging. Aim: The study was carried out to apply leg wrapping technique for the prevention of spinal-induced hypotension (SIH) during CS.Methods: Randomized Controlled Trial design was utilized at cesarean delivery operating room Mansoura General Hospital in El-Mansoura City during the period from May 2018 to November 2018. A purposive sample of 88 pregnant women, assigned randomly to an intervention group (n = 44) in which their legs wrapped with elastic crepe bandage and control group (n = 44) in which no wrapping was done. Data collected for maternal, neonatal hemodynamic and signs of hypotension, the feasibility of application and cost analysis.Results: There was a statistically significant difference in the incidence of SIH and Ephedrine use among both groups (18.20% in leg wrapping group whereas 75% in control group). In addition, neonatal acidosis and NICU admission were less among leg wrapping group (11.40%, 9.10% respectively). Economically, leg wrapping technique was cost effective compared to the cost of the hospital regimen for treating SIH and admission to (NICU).Conclusion and recommendations: Leg wrapping technique was cost effective and an efficient method for decreasing SIH, neonatal acidosis and Ephedrine administration. It is recommended to apply leg wrapping technique in maternal hospitals' protocol of care for decreasing SIH during CS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".