Antimicrobial Prophylaxis Practices for Obstetrical Infection Prevention in Alberta: A Multi-Method Study
Bibliographic record
Abstract
OBJECTIVES: Suboptimal antimicrobial prophylaxis in labour and delivery increases the risk of poor health outcomes. We examined Alberta's obstetrical antimicrobial prophylaxis practices to identify gaps and opportunities for clinical improvement. METHODS: We used human-centred design (mapping out patients' journey to delivery) and health care administrative data to establish a baseline of current antimicrobial prophylaxis practices for early-onset neonatal group B Streptococcus (GBS) disease and surgical site infection (SSI) prevention during labour and delivery. RESULTS: Seven obstetrical health care providers participated in the journey mapping process, which highlighted the need for improved allergy documentation. Specifically, the findings emphasized better penicillin allergy risk stratification, accurate electronic record-keeping, and identification of opportunities to de-label patients incorrectly identified as allergic. The analysis of 13 818 patients who delivered in Alberta hospital sites between November 1, 2022 and May 31, 2023 showed that most received appropriate antimicrobial prophylaxis for GBS and SSI prevention. Among the 140 GBS-positive patients with beta-lactam allergy who delivered vaginally, cefazolin (62%) was the most prescribed antibiotic, followed by clindamycin (26%) and penicillin (13%). Of the 58 patients with beta-lactam allergy needing SSI prophylaxis, most also received cefazolin. However, some cases still showed suboptimal use of second-line antibiotics. CONCLUSIONS: The journey map highlights the prenatal period and hospital admission as key phases for ensuring allergy documentation accuracy. Considering that inappropriate penicillin allergy labelling and administration of suboptimal antimicrobial prophylaxis persist, there is room for quality improvement initiatives to promote validation of allergy status and address the use of suboptimal antibiotics for penicillin allergy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".