P.028 Global surgery for pediatric hydrocephalus in the developing world: a review of the history, challenges, and future directions
Bibliographic record
Abstract
Background: Pediatric hydrocephalus is one of the most common neurosurgical conditions and is a major contributor to the global burden of surgically treatable diseases. Methods: The authors conducted a literature review around the topic of pediatric hydrocephalus in the context of global surgery, the unique challenges to creating access to care in low-income countries, and current international efforts to address the problem. Results: Developing countries face the greatest burden of pediatric hydrocephalus due to high birth rates and greater risk of neonatal infections. This burden is related to more general global health challenges, including malnutrition, infectious diseases, maternal and perinatal risk factors, and education gaps. Unique challenges pertaining to the treatment of hydrocephalus in the developing world include a preponderance of postinfectious hydrocephalus, limited resources, and restricted access to neurosurgical care. In the 21st century, several organizations have established programs that provide hydrocephalus treatment and neurosurgical training in Africa, Central and South America, Haiti, and Southeast Asia. These international efforts have employed various models to achieve the goals of providing safe, sustainable, and cost-effective treatment. Conclusions: Broader commitment from the pediatric neurosurgery community, increased funding, public education, surgeon training, and ongoing surgical innovation will be needed to meaningfully address the global burden of untreated hydrocephalus.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".