Probiotics and nutrients for the first 1000 days of life in the developing world
Bibliographic record
Abstract
Clinically proven probiotics are, for the most part, not available in the developing world and certainly not affordable for the majority of people. This is unconscionable considering these products can alleviate diarrhoea and various infections, which are by far the major cause of death in children and in adults who are HIV positive. Indeed, some of these products have been proven in developing world settings. Distribution networks exist along with pharmacies and clinics that dispense drugs and products that require refrigeration. So, are lack of profit or company resources the problem? Our university has shown that alternative community based kitchen models that produce probiotics can be established. These empower local people, are socially responsible, produce affordable products and deliver benefits to over 3,000 children and adults daily. Surely, other institutions and corporations can multiply this effect and develop social business models across the developing world that are supported by clinical and basic science studies? In this review, we will discuss the application of probiotics and selected nutrients in the first 1000 days of life, a critical timepoint which is particularly challenging in resource disadvantaged countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".