Affordable assessment of newborn brain health following perinatal asphyxia in East Africa: a pilot study (728.6)
Bibliographic record
Abstract
Perinatal asphyxia is a major cause of infant mortality in the developing world with 280,000 deaths per year in sub‐Saharan Africa alone. Brain injuries resulting from asphyxia can have detrimental effects on brain function, which can negatively impact quality of life in those who survive. Since clinical manifestations are often subtle and the therapeutic window is narrow, investigating effective diagnostics in low‐resource hospitals is imperative. We used affordable cranial ultrasonography (cUS) and electroencephalography (EEG) to assess how structural abnormalities and impairments in auditory processing are related to neurodevelopmental outcome in East African newborns. Healthy infants (N=15) and infants diagnosed with asphyxia (N=17) were recruited from urban hospitals in Rwanda and Kenya. Structural images of the brain were obtained by cUS through the anterior fontanel. EEG was recorded at rest and during three auditory stimulation tasks. We expect negative health outcomes to be correlated with enlarged lateral ventricle size, thalamus echodensity and the neural strength reflected in the EEG data. We will compare the prognostic value of these measures, after completing “offline” analyses, with clinical assessment of raw data. This study forms a solid foundation for further work in identifying early clinical markers of brain injury and establishing protocols that are viable in the developing world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".