VP50.04: The use of ASA for the prevention of pre‐eclampsia: healthcare provider survey
Bibliographic record
Abstract
The purpose of this study was to evaluate current practice and attitudes among Canadian obstetric care providers (Obstetricians/Gynecologists (OB/GYN), Maternal Fetal Medicine (MFM) specialists, Family physicians (FP) and Midwives (MW)) on screening for pre-eclampsia and the use of low dose aspirin (ASA) for the prevention of pre-eclampsia. A brief national-wide online survey was designed to assess the knowledge on pre-eclampsia screening and the usage of ASA for pre-eclampsia prevention. Descriptive statistics were used to characterize the participants' demographic characteristics. Chi-square tests were used to assess differences in health professionals' knowledge on PE screening and ASA usage for PE prevention between provider groups to identify key knowledge gaps. 336 respondents completed the survey: 55.1% OB/GYN,17.0% FP, 14.6%, MFM, 10.0% MW. Guidelines followed: SOGC 57.2%, ACOG 17%, NICE 7.5%, local guidelines 18.2%. Percentage of respondents who prescribe ASA for prevention of pre-eclampsia; 96.7%, prior to 16 weeks' gestation: 96.6%, recommended dose162 mg OD: 38% MFMs 61.2%, OB/GYN 48.6%, FP 45.6%, and MW 38.2%), recommended time of day to take ASA: bedtime 56.5%, recommended gestational age to stop ASA: 36 weeks gestation MFMs (87.8%), Ob/Gyns (71.9%), MWs (67.6%) and FPs (59.6%, p = 0.014). This study provides important information regarding the current practice among obstetric care providers in Canada with regard to pre-eclampsia screening and prevention. While most recommend ASA for the prevention of pre-eclampsia, the screening guidelines followed, ASA dosage and dosage schedules recommended, and timing of stopping of ASA were variable. This baseline knowledge will enable the focused development of educational materials for implementation of PE screening and prevention based on recent high grade evidence regarding predictive screening algorithms and optimal use of ASA for prevention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".