Gestational weight gain and the risk of infant mortality amongst women with normal prepregnancy BMI: the Friedmann-Balayla model
Bibliographic record
Abstract
Objective: The study of the association between gestational weight gain (GWG) and infant mortality is riddled with methodological concerns, particularly with limitations in accounting for gestational age-specific weight gain. In our study, we developed a new model, which accounts for gestational age, to determine whether insufficient or excessive GWG is associated with an increased risk of infant death amongst women with normal prepregnancy BMI (18.5–24.9 kg/m2).Methods: We developed and implemented the Friedmann-Balayla model to mitigate gestational age-related biases in our assessment, and conducted a population-based cohort study using the CDC’s 2013 Period-Linked Birth-Infant Death data. The impact of GWG according to the 2009 IOM guidelines on the risk of infant mortality was estimated using logistic regression analysis, adjusting for relevant confounders.Results: Our cohort consisted of 1,517,525 singleton deliveries and 6138 infant deaths. Overall, relative to women achieving adequate GWG, neither women gaining insufficient nor excessive weight had greater odds of infant death during the first year of life (OR [95%CI]): 1.06 [0.97–1.17] (p = 0.174), and 0.98 [0.91–1.04] (p = 0.523), respectively. This relationship did not change when restricting our analysis to term or preterm deliveries or when conducting sensitivity analyses accounting for maternal morbidities (p > 0.05).Conclusion: Using this novel analytic approach, there does not appear to be an increased risk of infant mortality if GWG falls outside of the IOM guidelines in women with normal prepregnancy BMI. Future studies should apply this methodology to other BMI categories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".