A conceptual framework of the impact of maternal early life drought exposure on newborn size in Malawi
Bibliographic record
Abstract
The effects of adverse prenatal conditions are not only experienced over the life course but can be passed on intergenerationally. The present study took advantage of a natural experiment from three drought periods of 1981/82, 1987/88, and 1992/93 that occurred in Malawi with varying severity and used data from a randomized clinical trial (RCT), conducted between 2011–2015 (Protocol #NCT01239693). The present study aimed to assess the effect of the interactions between maternal exposure to drought in early life and prenatal supplementation with a novel supplement [small quantity (SQ), lipid-based nutrient supplement (LNS)], the standard of care prenatal supplement [iron-folic acid or IFA], or a close substitute of the standard of care [multiple micronutrients or MMN], on subsequent infant birth outcomes. During data analysis, ordinary least squares were used to run multiple regressions. The regression results were as follows. When there was no maternal exposure to drought, SQ-LNS compared to IFA appeared to improve subsequent infant birth outcomes for length-for-age Z score or LAZ (0.403 standard deviation (SD), Confidence interval CI [0.099, 0.708]), for subsequent infant weight-for-age Z score or WAZ (0.372 SD, CI [0.053, 0.691]), and for imputed infant birthweight or BTW (125.900 g, CI [2.901, 248.899]). In conclusion, the results show a pattern emerging whereby some positive associations can be observed, specifically, when maternal non-drought exposure variables and the SQ-LNS variable interact. Their combined effects on subsequent infant birth outcomes notably subsequent infant LAZ, subsequent infant WAZ, and subsequent infant imputed BWT appear to be positive.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".