The CURE Protocol: evaluation and external validation of a new public health strategy for treating paediatric hydrocephalus in low-resource settings
Bibliographic record
Abstract
Introduction: Managing paediatric hydrocephalus with shunt placement is especially risky in resource-limited settings due to risks of infection and delayed life-threatening shunt obstruction. This study evaluated a new evidence-based treatment algorithm to reduce shunt-dependence in this context. Methods: A prospective cohort design was used. The CURE Protocol employs preoperative and intraoperative data to choose between endoscopic treatment and shunt placement. Data were prospectively collected for 730 children in Uganda (managed by local neurosurgeons highly experienced in the protocol) and, for external validation, 96 children in Nigeria (managed by a local neurosurgeon trained in the protocol). Results: The age distribution was similar between Uganda and Nigeria, but there were more cases of postinfectious hydrocephalus in Uganda (64.2% vs 26.0%, p<0.001). Initial treatment of hydrocephalus was similar at both centres and included either a shunt at first operation or endoscopic management without a shunt. The Nigerian cohort had a higher failure rate for endoscopic cases (adjusted HR 2.5 (95% CI 1.6 to 4.0), p<0.001), but not for shunt cases (adjusted HR 1.3 (0.5 to 3.0), p=0.6). Despite the difference in endoscopic failure rates, a similar proportion of the entire cohort was successfully treated without need for shunt at 6 months (55.2% in Nigeria vs 53.4% in Uganda, p=0.74). Conclusion: Use of the CURE Protocol in two centres with different populations and surgeon experience yielded similar 6-month results, with over half of all children remaining shunt-free. Where feasible, this could represent a better public health strategy in low-resource settings than primary shunt placement.
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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 | 0.200 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".