Maternal height associated with cesarean section. A cross-sectional study using the 2014–2015 national maternal-child health survey in Guatemala
Bibliographic record
Abstract
Abstract Background Socioeconomic status is associated with cesarean section (CS). Maternal height, however, may be another related factor to CS. In Guatemala, a quarter of women between 15 and 49 years of age are shorter than 145 cm. Therefore, this study aims to examine the association of maternal height with cesarean section in Guatemala. Methods We carried out a secondary analysis study using data from the 2014–15 Guatemalan national maternal and child health survey—9542 mothers aged 15–49 and 12,426 live births were analyzed. We obtained the prevalence ratio of the association between maternal height and CS based on three Poisson regression models. One model included all live births, another the first live birth, and a third model the last live birth. For each model, we accounted for covariates and sampling design. Results The national prevalence of CS was 26.3% (95%CI: 25.0, 27.7). The adjusted prevalence ratio of CS, including all live births, was 1.63 (95%CI: 1.37, 1.94) more likely in mothers shorter than 145 cm compared with those equal or greater than 170 cm. This figure was 1.45 (95%CI: 1.19, 1.76) in the model with the first live birth. In the model with the last birth, maternal height was not associated with CS after accounting for previous CS as one of the covariates. Conclusions Prevalence of CS in this setting was high and above international recommendations. Further, very short mothers were more likely to experience CS compared to taller mothers after accounting for covariates, except when a previous CS was present. Maternal height should be included in clinical assessments during prenatal care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".