Precipitation Modeling and Contract Valuation
Bibliographic record
Abstract
OBJECTIVE--To investigate if psychological distress during pregnancy is associated with increased risk of preterm delivery. DESIGN--Prospective, population based, follow up study with repeated measures of psychological distress (general health questionnaire), based on the use of questionnaires. SETTING--Antenatal care clinic and delivery ward, Aarhus University Hospital, Denmark. SUBJECTS--8719 women with singleton pregnancies attending antenatal care for the initial visit between 1 August 1989 and 30 September 1991; 5872 women (67%) completed all questionnaires. MAIN OUTCOME MEASURE--Preterm delivery. Estimation of gestational age at delivery was mainly based on early ultrasound measurements. RESULTS--In 197 cases (3.6%) the woman delivered prematurely (less than 259 days). A dose-response relation between psychological distress in the 30th week of pregnancy and risk of preterm delivery was found, but distress measured in the 16th week was not related to preterm delivery. Control of confounding was secured by the use of multivariate logistic regression models. Relative risk for preterm delivery was 1.22 (95% confidence interval 0.84 to 1.79) for moderate distress and 1.75 (1.20 to 2.54) for high distress in comparison to low distress. CONCLUSIONS--Psychological distress later in pregnancy is associated with an increased risk of preterm delivery. Future interventional studies should focus on ways of lowering psychological distress in late pregnancy.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".