Missing paternal data and adverse birth outcomes in Canada.
Bibliographic record
Abstract
BACKGROUND: Research on predictors of birth outcomes tends to focus on maternal characteristics. Less is known about the role of paternal factors. Missing paternal data on administrative records may be a marker for risk of adverse birth outcomes. DATA AND METHODS: Analyses were performed on a cohort of births that occurred from May 16, 2004 through May 15, 2006, which was created by linking birth and death registration data with the 2006 Canadian census. Log-binomial and binomial regression were used to estimate relative risks and risk differences for preterm birth, small-for-gestational-age birth, stillbirth and infant mortality associated with the absence of paternal information. Analyses controlled for maternal age, education, household income, parity, marital status, ethnicity and birthplace. RESULTS: The analyses pertained to 135,285 singleton births. Paternal data were missing from the birth registration for 7,461 births (4.6%) and from the census data for 17,713 births (11.4%). The adjusted relative risks associated with missing paternal data on the birth registration were 1.12 (95% CI: 0.99, 1.26) for preterm birth; 1.15 (1.05, 1.26) for small-for-gestational-age birth; 1.86 (1.27, 2.73) for stillbirth; and 1.53 (1.00, 2.34) for infant mortality. Estimates were robust to varying definitions of missing paternal information, based on the birth registration, census data, or both. INTERPRETATION: This study suggests that missing paternal data is a marker for increased risk of adverse birth outcomes, over and above maternal characteristics.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".