Neonatal neurological testing in resource-poor settings
Bibliographic record
Abstract
The aim of the study was to design and test a neurological examination for newborns that could be performed reliably by paramedical staff in resource-poor settings. The examination was adapted from a method established by Dubowitz et al., the latest version of which includes an optimality score. The final items in the test were chosen because they were culturally acceptable, could be elicited according to strict but easily comprehensible instructions and because the expected responses could be scored by the descriptions given or by diagrams in the proforma. The shortened examination was easily taught to paramedical staff who achieved a high degree of inter-observer reliability. This shortened version of the examination was piloted by comparing newborns from a Karen refugee camp on the western border of Thailand and from a large maternity hospital in Bangkok with a standardized cohort of newborns in London. The modified shortened version of the test was sufficiently sensitive to identify a number of differences between the cohorts, notably the poor vision performance and markedly reduced tone of the Karen newborns. In conclusion, the test can be used very reliably by paramedical staff and is a useful, simple and portable tool for the neurological assessment of newborn babies where resources are limited.
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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 | 0.003 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".