Vitamin Deficiencies Are Both Risk Factors and Protective Against Diverse Urogenital and Intestinal Infections in Pregnant Ngabe Women from Panama
Bibliographic record
Abstract
We enrolled 213 pregnant Ngabe women in a cross‐sectional study in Western Panama. Clinical diagnosis was done for respiratory, oral, skin and vaginal infections, along with laboratory diagnosis of infections in urine, fecal and vaginal samples. Serum blood samples were analyzed for concentrations of Vitamins A, D, B 12 , folate and ferritin. The most common infections were bacterial vaginosis (BV) (91%), hookworm (57%), asymptomatic bacteriuria (AB) (56%), Ascaris (33%), candidiasis (25%), dental caries (20%), scabies (17%), and trichomoniasis (17%). Vitamin deficiencies were also common: B 12 (85%), ferritin (83%), D (65%), A (41%), folate (24%); 13% of women had 4 of the 5 deficiencies. Multiple regression analysis revealed that Ascaris intensity was positively associated with gingivitis, fungal infections of the skin and vaginal tract, and AB (adjusted R 2 = 0.23) but not with any of the micronutrients. The severity of BV was elevated in those with vitamin deficiencies (D, folic acid, low hemoglobin) but was reduced by vaginal candidiasis. Surprisingly, high vitamin D, folic acid and hemoglobin were protective against severe trichomoniasis after controlling for the effect of other infections. These results highlight the mix of synergistic and antagonistic associations among infections and micronutrient deficiencies in this vulnerable population. Funded by SENACYT (Panama) and McGill Vitamin Fund
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".