EP08.08: Improving estimated fetal weight accuracy at a tertiary centre: a quality improvement intervention
Bibliographic record
Abstract
Implementation of improvement strategies targeting identified error drivers of EFW identified by a quality assurance project in a tertiary healthcare centre in Canada. Based on previous reported quality improvement project at our centre, we proposed a lean goal of improving the error by 15% by sonographer education and incorporation of feedback. We audited our population 6 months after the intervention. 2 PDSA cycles were conducted. Images obtained within 14 days from delivery were analysed by independent examiner. The framework from 2019 ISUOG biometry guidelines scoring was used. Results were reported as descriptive statistics. Pre- and post-intervention were compared by T-test (P<.05). EFW error was found in 81 (5%) from 20% preintervention. When analysing images individually and comparing EFW parameters head-to-head for the scans with error, there were statistically significant improvements. Accurate EFW is critical for clinical decision-making and is a quality measure in antenatal care. Identifying key sonographic error drivers, education and feedback led to significant improvement.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".