Evaluating Affordable Cranial Ultrasonography in East African Neonatal Intensive Care Units
Bibliographic record
Abstract
Neuroimaging is a valuable diagnostic tool for the early detection of neonatal brain injury, but equipment and radiologic staff are expensive and unavailable to most hospitals in developing countries. We evaluated an affordable, portable ultrasound machine as a quantitative and qualitative diagnostic tool and to establish whether a novice sonographer could effectively operate the equipment and obtain clinically important information. Cranial ultrasonography was performed on term healthy, pre-term and term asphyxiated neonates in Rwandan and Kenyan hospitals. To evaluate the detection of ventriculomegaly and compression injuries, we measured the size of the lateral ventricles and corpus callosum. The images were also assessed for the presence of other cerebral abnormalities. Measurements were reliable across images, and cases of clinically relevant ventriculomegaly were detected. A novice sonographer had good-to-excellent agreement with an expert. This study demonstrates that affordable equipment and cranial ultrasound protocols can be used in low-resource settings to assess the newborn brain.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".