Scientific Rationale for Study Design of Community-based Simplified Antibiotic Therapy Trials in Newborns and Young Infants With Clinically Diagnosed Severe Infections or Fast Breathing in South Asia and sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Newborns and young infants suffer high rates of infections in South Asia and sub-Saharan Africa. Timely access to appropriate antibiotic therapy is essential for reducing mortality. In an effort to develop community case management guidelines for young infants, 0-59 days old, with clinically diagnosed severe infections, or with fast breathing, 4 trials of simplified antibiotic therapy delivered in primary care clinics (Pakistan, Democratic Republic of Congo, Kenya and Nigeria) or at home (Bangladesh and Nigeria) are being conducted. METHODS: This article describes the scientific rationale for these trials, which share major elements of trial design. All the trials are in settings of high neonatal mortality, where hospitalization is not feasible or frequently refused. All use procaine penicillin and gentamicin intramuscular injections for 7 days as reference therapy and compare this to various experimental arms utilizing comparatively simpler combination regimens with fewer injections and oral amoxicillin. CONCLUSION: The results of these trials will inform World Health Organization policy regarding community case management of young infants with clinical severe infections or with fast breathing.
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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 | 0.420 | 0.464 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.020 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".