The global problem of childhood diarrhoeal diseases: emerging strategies in prevention and management
Bibliographic record
Abstract
Acute diarrhoeal diseases remain a leading cause of global morbidity and mortality particularly among young children in resource-limited countries. Recent large studies utilizing case–control design, prospective sampling and more sensitive and broad diagnostic techniques have shed light on particular pathogens of importance and highlighted the previously under recognized impact of these infections on post-acute illness mortality and growth. Vaccination, particularly against rotavirus, has emerged as a key effective means of preventing significant morbidity and mortality from childhood diarrhoeal disease. Other candidate vaccines against leading diarrhoeal pathogens, such as enterotoxigenic Escherichia coli and Shigella spp., also hold significant promise in further ameliorating the burden of enteric infections in children. Large studies are also currently underway evaluating novel and potential easy-to-implement water, sanitation and hygiene (WASH) preventive strategies. Given the ongoing global burden of this illness, the paucity of new advances in case management over the last several decades remains a challenge. The increasing recognition of post-acute illness mortality and growth impairment has highlighted the need for interventions that go beyond management of dehydration and electrolyte disturbances. The few trials of novel promising interventions such as probiotics have mainly been conducted in high-income settings. Trials of antimicrobials have also been primarily conducted in high-income settings or in travellers from high-income settings. Bloody diarrhoea has been shown to be a poor marker of potentially treatable bacterial enteritis, and rising antimicrobial resistance has also made empiric antimicrobial therapy more challenging in many settings. Novel effective and sustainable interventions and diagnostic strategies are clearly needed to help improve case management. Diarrhoeal disease and other enteric infections remain an unmet challenge in global child health. Most promising recent developments have been focused around preventive measures, in particular vaccination. Further advances in prevention and case management including the possible use of targeted antimicrobial treatment are also required to fully address this critical burden on child health and human potential.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".