Bangladesh Environmental Enteric Dysfunction (BEED) study: protocol for a community-based intervention study to validate non-invasive biomarkers of environmental enteric dysfunction
Bibliographic record
Abstract
INTRODUCTION: Environmental enteric dysfunction (EED) is a subacute inflammatory condition of the small intestinal mucosa with unclear aetiology that may account for more than 40% of all cases of stunting. Currently, there are no universally accepted protocols for the diagnosis, treatment and ultimately prevention of EED. The Bangladesh Environmental Enteric Dysfunction (BEED) study is designed to validate non-invasive biomarkers of EED with small intestinal biopsy, better understand disease pathogenesis and identify potential therapeutic targets for interventions designed to control EED and stunting. METHODS AND ANALYSIS: ). After screening, participants eligible for study provide faecal, urine and plasma specimens to quantify the levels of candidate EED biomarkers before and after receiving a nutritional intervention. Participants who fail to respond to nutritional therapy are considered as the candidates for upper gastrointestinal endoscopy with biopsy. Histopathological scoring for EED will be performed on biopsies obtained from several locations within the proximal small intestine. Candidate EED biomarkers will be correlated with nutritional status, the results of histochemical and immunohistochemical analyses of epithelial and lamina propria cell populations, plus assessments of microbial community structure. ETHICS AND DISSEMINATION: Ethics approval was obtained in all participating institutes. Results of this study will be submitted for publication in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov ID: NCT02812615. Registered on 21 June 2016.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.015 |
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".