Effects of Early Neonatal Administration of Lactiplantibacillus plantarum ATCC 202195 on the Fecal Microbiota of Infants in Dhaka, Bangladesh
Bibliographic record
Abstract
Objectives: Biomarker measurements are subject to intra-individual variation but the implications for prevalence assessment are uncertain.We assessed agreement between repeated biomarker measurements (3-wk interval) and whether adjustment for intra-individual variation influenced prevalence estimates.Methods: Participants were children 24-59 mo of age enrolled in the control group of a trial in northern Ghana, whose households received iodine-fortified bouillon cubes for 9 mo.Children with biomarker data at intervention wks 35 and 38 were included (n316).Hemoglobin (Hb) was measured in venous blood by Hemocue 301+.Plasma ferritin (Fer) and soluble transferrin receptor (sTfR) were assessed by ELISA; Fer was adjusted for inflammation.Agreement was assessed by Spearman correlations (rho), Bland-Altman plots, and t-tests.We compared anemia (Hb < 11 g/L) and iron deficiency (ID, Fer < 12 g/L or sTfR > 8.3 mg/L) between time points by Chi-square test and calculated percent agreement.The National Cancer Institute method, via the SIMPLE macro, was used to estimate "usual" biomarker concentration and prevalence of anemia and ID.Results: Biomarker concentrations at the two time points were correlated (Hb: rho0.78,Fer: rho0.84;sTfR: rho0.91;all P< 0.001) and means did not differ (P >0.05).Anemia was 19.9% at wk35 and 19.1% at wk38, and agreement was 84.5%; 11.6% (n36) had anemia at both time points.ID based on Fer was 53.2% at wk35, 51.6% at wk38, and agreement was 83.2% (44% with ID at both time points); for sTfR, corresponding values were 52.2%, 51.6%, and 88.6% (46% with ID at both time points).Intra-individual variation comprised 22.1% of total variation in Hb concentration, 17.7% for Fer, and 7.4% for sTfR.Prevalence of anemia based on the estimated "usual" Hb distribution was 20.8%, and "usual" ID was 49.6% for Fer and 57.7% for sTfR.Conclusions: Repeated measurements of Hb and iron biomarkers and related prevalences showed overall agreement in this sample, although ~11-17% of children changed status after three weeks.Adjusting the biomarker distribution for intra-individual variation had little impact on estimated prevalence of anemia or ID, suggesting that a single measure is sufficient for prevalence estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".