A call to bridge the diagnostic gap: diagnostic solutions for neonatal sepsis in low- and middle-income countries
Bibliographic record
Abstract
The first month of life is the most critical period for an infant’s survival, yet the most neglected for the provision of quality care. Each year, an estimated 2.3 million neonates die in their first month of life. 1 Sepsis alone is responsible for 7.3% of all neonatal deaths worldwide, with a significant burden falling on low- and middle-income countries (LMICs).2 While there remains an ongoing debate regarding the definition of neonatal sepsis, it is broadly described as a suite of non-specific signs that may include fever or hypothermia, respiratory distress, cyanosis and apnoea, feeding difficulties, lethargy or irritability, hypotonia, seizures, bulging fontanelle, poor perfusion, bleeding problems, abdominal distention, hepatomegaly, unexplained jaundice or more importantly ‘just not looking right’.3
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.028 | 0.037 |
| Insufficient payload (model declined to judge) | 0.049 | 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".