Oxytocin–ergometrine co‐administration does not reduce blood loss at caesarean delivery for labour arrest*
Bibliographic record
Abstract
OBJECTIVE: To determine if intravenous infusion of a combination of oxytocin and ergometrine maleate is better than oxytocin alone to decrease blood loss at caesarean delivery for labour arrest. DESIGN: Prospective, double-blinded, randomised controlled trial. SETTING: Mount Sinai Hospital, Toronto, Canada. POPULATION: Women undergoing caesarean deliveries for labour arrest. METHODS: Forty-eight women were randomised to receive infusion of either ergometrine maleate 0.25 mg + oxytocin 20 iu or oxytocin 20 iu alone, diluted in 1 l of lactated Ringer's Solution, immediately after delivery of the infant. Unsatisfactory uterine contractions after delivery were treated with additional boluses of the study solution or rescue carboprost. Blood loss was estimated based on the haematocrit values before and 48 hours after delivery. MAIN OUTCOME MEASURES: The primary outcome was the estimated blood loss, while the secondary outcomes included the use of additional uterotonics, need for blood transfusion and the incidence of adverse effects. RESULTS: The estimated blood loss was similar in the oxytocin-ergometrine and oxytocin-only groups; 1218 +/- 716 ml and 1299 +/- 774 ml, respectively (P = 0.72). Significantly fewer women required additional boluses of the study drug in the oxytocin-ergometrine group (21 and 57%; P = 0.01). Nausea (42 and 9%; P = 0.01) and vomiting (25 and 4%; P = 0.05) were significantly more prevalent in the oxytocin-ergometrine group. CONCLUSIONS: In women undergoing caesarean delivery for labour arrest, the co-administration of ergometrine with oxytocin does not reduce intraoperative blood loss, despite apparently superior uterine contraction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".