P14.11: Patient factors affecting the quality of routine second trimester obstetrical ultrasound images
Bibliographic record
Abstract
To determine patient factors that affect US image quality, including the use of skin creams. This is a prospective observational study with institutional ethics approval. Women presenting for routine second trimester US scan were invited to complete a survey concerning their age, height, weight, ethnicity, previous abdominal surgery, and if skin cream was used on their abdomen, the frequency, last application and duration of use. Standard images from each case (BPD, cerebral ventricles, four chamber heart and abdominal circumference) were rated separately on a score of 1–5 (5-best) by two readers. Scoring was based on a previously agreed upon template. The readers were blinded to the patients' information. The average of the two readers' scores was calculated for each patient. Statistical analyses involved T-test, Spearman's rank correlation, intraclass correlation coefficient (ICC) and multiple regression analysis. Between Dec. 30, 2003 and Feb. 4, 2004, we studied 92 women. Median maternal age was 32.3 years; median gestational age 19.2 weeks (17.6–23.9); median body mass index (BMI) 22.5 (15.0–40.9); 69 women (75%) used skin cream; 15 (16%) had lower abdominal surgery; and 74 (80%) were “white”. The average US score was 2.4. There was substantial agreement between the two readers (ICC = 0.75). In univariate analyses, US score was associated negatively with BMI (rank correlation = − 0.6; p < 0.0001) and previous surgery (P = 0.03) and positively with skin cream use (P = 0.004). In multiple regression analysis, statistically significantly higher US scores were seen with low BMI (p < 0.01); and “white” ethnicity (P = 0.02). Skin cream use, previous surgery and gestational age were not significant predictors of US score. Those with lower BMI were more likely to use creams. BMI and ethnicity were significant predictors of US image quality score. Adjusting for BMI, skin creams did not appear to significantly affect US quality.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".