Patterns of Prior Induced Abortions and the Likelihood of Subsequent Natural Pregnancy Loss: Exploratory Application of Pregnancy Outcome Sequencing
Bibliographic record
Abstract
BACKGROUND: Research concerning the long-term health consequences of induced abortion is constrained by both the limitations in the availability of data necessary to construct complete reproductive histories, as well as the limitations in the analytical methods necessary to interpret them. OBJECTIVE: This study aims to determine the association of induced abortion and the likelihood of a subsequent natural loss by applying the pregnancy outcome sequence (POS), a research construct that defines the number and order of all pregnancy outcomes (births, induced abortions, natural losses) in each woman's reproductive history. METHODS: Using the Medicaid Analytic eXtract files from the Centers for Medicare and Medicaid Services Chronic Conditions Warehouse, we identified a study population of 508 unique POSs, representing 5455 women, each of whom had 1 to 16 pregnancy outcomes, for a total of 14,198 pregnancies. We applied an exploratory iterative sequential analytical approach, which included aggregate POS correlation analysis, logistic multiple regression, and simultaneous CIs (Agresti and Tukey-Kramer methods). We also established counting methods to populate the data tables for each analytical phase. RESULTS: Overall, we found evidence to conclude that both prior abortions and natural losses are significantly associated with the risk of subsequent natural losses. For abortion, there is evidence of a dose-response relationship from 0 to 3 abortions and the likelihood of subsequent natural loss. For natural loss, the risk of a subsequent natural loss is significant after 2 natural losses and between the first and second. There is no association of prior births, or any combination of birth/abortion or birth/natural loss, with the risk of subsequent natural loss. Limitations imposed by the skewed distribution of the number of total pregnancies per reproductive history, resulting in small cell sizes, and exclusion of important covariates, restrain assurance in the results. CONCLUSIONS: The POS demonstrates that the order and combinations of pregnancy outcomes may result in varying conclusions that were previously undetectable. Therefore, further methodological development is indicated.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".