Maternal Hemoglobin Concentration and Pregnancy Outcome: A Study of the Effects of Elevation in El Alto, Bolivia
Bibliographic record
Abstract
Iron-defciency anemia is often under-diagnosed in developing coun- tries, specifcally in pregnant populations in regions of high altitude. Hemoglobin levels are not consistently adjusted for elevation, and therefore many anemic pa- tients are left undiagnosed. The purpose of this study was to incorporate current parameters for diagnosing anemia in pregnancy at high altitudes, and to evaluate the effects of appropriately adjusted hemoglobin concentrations on pregnancy out- come. A few studies have examined the effect of elevation on hemoglobin status, and other studies have considered the effects of anemia of pregnancy; however, there is a lack of data demonstrating that altitude-adjusted hemoglobin levels ac- curately predict pregnancy outcome. Using the Student t-Test, multiple linear re- gression, and ANOVA statistical analyses, various factors of pregnancy outcome were compared between anemic and non-anemic groups, as defned by hemoglobin cut-off levels adjusted for trimester of pregnancy and altitude. when appropriate adjustments were used, maternal anemia was associated with lower infant Apgar scores at both one minute and fve minutes after birth, as well as complication of labor, lower gestational age at birth, and higher parity. This study demonstrates the importance of altitude and trimester specifc adjustments to maternal hemoglobin levels in order to accurately diagnose anemia in pregnancy. In addition, a clear cor- relation is seen between maternal hemoglobin level and pregnancy outcome.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".