Interactions Among Urogenital, Intestinal, Skin, and Oral Infections in Pregnant and Lactating Panamanian Ngäbe Women: A Neglected Public Health Challenge
Bibliographic record
Abstract
Interrelationships among bacteria, protozoa, helminths, and ectoparasites were explored in a cross-sectional survey of 213 pregnant and 99 lactating indigenous women. Prevalences in pregnancy and lactation, respectively, were: vaginitis (89.2%; 46.8%), vaginal trichomoniasis (75.3%; 91.1%), bacterial vaginosis (BV; 60.6%; 63.3%), hookworm (56.6%; 47.8%), asymptomatic bacteriuria/urinary tract infection (AB/UTI; 56.2%; 36.2%), cervicitis (33.3%; 6.3%), vaginal yeast (24.9%; 11.4%), Ascaris (32.5%; 17.4%), vaginal diplococci (20.4%; 31.6%), caries (19.7%; 18.2%), scabies (17.4%; 8.1%), and Trichuris (12.5%; 8.7%). Multiple regressions revealed positive associations during pregnancy (trichomoniasis and AB/UTI; diplococci and Ascaris) and lactation (yeast and scabies). Negative associations were detected in pregnancy (BV and trichomoniasis; hookworm and diplococci) and lactation (BV and yeast). Vaginal Lactobacillus reduced odds of diplococci in pregnancy and lactation, but increased Ascaris eggs per gram (epg) and odds of trichomoniasis in pregnancy and yeast in lactation. These associations raised a concern that treatment of one condition may increase the risk of another.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".