Cost Implications of Using Carbetocin Injection to Prevent Postpartum Hemorrhage in a Canadian Urban Hospital
Bibliographic record
Abstract
OBJECTIVE: Recent World Health Organization (WHO) recommendations regarding uterotonics for the prevention of postpartum hemorrhage (PPH) state that carbetocin should be considered a first-line prophylactic agent for all births where its cost is comparable to other effective uterotonics. This study evaluated whether a room temperature stable formulation of carbetocin met this recommendation in a Canadian urban hospital setting. METHODS: A decision tree model was developed to assess the financial implications of replacing oxytocin with carbetocin as a first-line prophylactic agent for PPH prevention in a Greater Toronto Area (GTA) hospital. The analysis accounted for the mode of delivery, efficacies of carbetocin and oxytocin in PPH prevention, occurrence of PPH-related health outcomes, and health care resource costs for PPH interventions. RESULTS: This study found that a GTA hospital, with 3242 deliveries per year, could save over CAD $349 000 annually by switching to room temperature stable carbetocin for PPH prevention. Carbetocin was able to lower institution costs by reducing the use of health care resources for PPH management in low-risk and high-risk PPH patients. The cost-saving potential of carbetocin relative to oxytocin was largely attributed to its greater efficacy in preventing the consequences of PPH. CONCLUSION: The use of room temperature stable carbetocin as a first-line prophylactic agent for PPH prevention meets WHO recommendations regarding uterotonics for PPH in a GTA hospital. The model from this study can be used to determine the financial impact of switching from oxytocin to carbetocin in other jurisdictions while diversifying a hospital's pool of PPH prophylactic agents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".