Decision Analysis Studies Involving the Management of Pregnant Women: A Systematic Review [13R]
Bibliographic record
Abstract
INTRODUCTION: This study systematically reviewed Decision Analysis (DA) and economic evaluation (EE) studies involving the management of pregnant women and described methodological variations and limitations. METHODS: Three databases were searched to include articles from inception to December 2014. DA and EE studies describing conditions involving the management of pregnant women were included and screening and data-extraction were conducted in duplicate. Outcomes included type of analysis, model type, input variables (“probabilities,” “utilities” and “costs”) and outcome variables (clinical or cost-based, time horizon). Reporting quality was assessed by a five-point scale. RESULTS: 6,055 titles were screened and 303 full texts retrieved, of which 57 describing management of clinical conditions were included. Five used Markov Models, while 52 used decision trees. Most studies (45) involved EE. Probabilities were obtained from multiple publications in 48 studies. Utilities were used in 26 studies [maternal and offspring (18), offspring (5) and combined (3)]. Utilities were obtained from physicians (6), physician and patient (3), mother and offspring (2), mother (2), public (2), parents (2) and offspring (1). Costs were obtained from perspectives of society (10), health care system (7), third-party payer (6), hospital (5) and multiple sources (2). 53 studies described clinical outcomes and 4 only described costs. Time-horizon involved maternal and/or offspring lifetime (19), pregnancy duration (19) and other reported time-horizons (6). Only five studies had a high risk-of-bias. CONCLUSION: There is considerable variation in the conduct and reporting of DA and EE studies in obstetrics. A consensus statement to standardize conduct of these studies in obstetrics is required.
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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 | 0.044 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".