Bibliographic record
Abstract
Background and objectives \nUltrasound is commonly used in pregnancy and serves a very important purpose in maternal and fetal screening and diagnosis. However, when not medically necessary it may have an economic impact and may lead to unnecessary interventions. The purpose of this dissertation was to increase the knowledge about prenatal ultrasound utilization which has not been adequately explored. The specific objectives of this dissertation were to 1) study the relationship between having single versus multiple prenatal care providers and the number of prenatal ultrasounds in the USA, 2) explore the factors associated with the timing of the first prenatal ultrasound in Canada, and 3) assess the relationship between the number of prenatal ultrasounds and primary caesarean delivery in Canada and the USA. \n \nMethods \nResponses from two national, cross-sectional surveys were analyzed to address these objectives. The two surveys were the Maternity Experiences Survey from Canada and the Listening To Mothers III survey from the USA. Negative binomial (for objective 1), multinomial (objective 2) and binary (for objective 3) logistic regression models were used to analyze the data. The provided survey weights were applied to both surveys to make the data nationally representative. Bootstrap weights were also applied to the analyses involving the Maternity Experiences Survey. \n \nResults \nThe results of objective 1 showed no significant relationship between having single versus multiple prenatal care providers and the number of prenatal ultrasounds in the USA. The findings of objective 2 showed that multiple factors were associated with the timing of prenatal ultrasound in Canada including province of prenatal care, maternal age and country of birth. The results of objective 3 showed a significant relationship between the number of prenatal ultrasounds and caesarean delivery in Canadian multiparas and primiparas as well as in American multiparas. \n \nConclusions \nThe findings of this dissertation form a baseline of attributes of prenatal ultrasound utilization in Canada and the USA and may be used to inform efforts aimed at the optimization of prenatal ultrasound utilization. Future studies can further investigate these relationships, perhaps using more robust databases that may allow for better control of confounding variables.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".