Impact of prenatal screening on future resource utilization in pregnancy
Bibliographic record
Abstract
In 2007, the Society of Obstetricians and Gynaecologists of Canada first recommended that prenatal screening for fetal aneuploidy be offered to all pregnant women. This is an important change in clinical obstetrics as previously these services had only been routinely offered to women who would be 35 or older at the time of delivery; however, maternal age alone is a poor predictor of fetal aneuploidy. Obtaining reliable data on prenatal screening utilization is problematic in Canada as these data are not collected in a systematic way nationally and, in many cases, not even provincially. This study aimed to understand utilization of prenatal screening in an Albertan context; to examine the impact of different prenatal screening strategies on health care utilization in pregnancy; and to estimate the incremental cost of prenatal screening when accounting for all of direct medical costs of this procedure including ancillary service use. Data on health resource utilization that occurred in the three months prior to pregnancy, during pregnancy and three months post-partum were collected from twelve unique clinical and administrative databases. Using deterministic linkage, participants from multiple databases were linked based on personal health number, gender and date of birth. This study found that utilization of prenatal screening tends to impact utilization of prenatal diagnostic services but not overall health resource utilization, and that there is a cost for prenatal screening but this cost may be less than what is suggested in the literature due to differences between hypothetical utilization patterns and actual resource utilization. It also suggests that current techniques used to identify women who have an increased risk of carrying a fetus with aneuploidy, may also be useful for identifying women who are at an increased risk of a variety of adverse pregnancy outcomes. With the rapid development of non-invasive prenatal testing, additional clinical changes in prenatal screening and diagnosis are likely to occur in the next few years. However, prior to abandoning screening in favour of non-invasive prenatal testing, the results of this work suggest that a more thorough evaluation of the impact of prenatal screening on non-aneuploidy outcomes may be warranted.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".