Towards Effective Ponseti Clubfoot Care: The Uganda Sustainable Clubfoot Care Project
Bibliographic record
Abstract
Neglected clubfoot is common, disabling, and contributes to poverty in developing nations. The Ponseti clubfoot treatment has high efficacy in correcting the clubfoot deformity in ideal conditions but is demanding on parents and on developing nations’ healthcare systems. Its effectiveness and the best method of care delivery remain unknown in this context. The 6-year Uganda Sustainable Clubfoot Care Project (USCCP) aims to build the Ugandan healthcare system's capacity to treat children with the Ponseti method and assess its effectiveness. We describe the Project and its achievements to date (March 2008). The Ugandan Ministry of Health has approved the Ponseti method as the preferred treatment for congenital clubfoot in all its hospitals. USCCP has trained 798 healthcare professionals to identify and treat foot deformities at birth. Ponseti clubfoot care is now available in 21 hospitals; in 2006–2007, 872 children with clubfeet were seen. USCCP-designed teaching modules on clubfoot and the Ponseti method are in use at two medical and three paramedical schools. 1152 students in various health disciplines have benefited. USCCP surveys have (1) determined the incidence of clubfoot in Uganda as 1.2 per 1000 live births, (2) gained knowledge surrounding attitudes, beliefs, and practices about clubfoot across different regions, and (3) identified barriers to adherence to Ponseti treatment protocols. USCCP is now following a cohort of treated children to evaluate its effectiveness in the Ugandan context. Level of Evidence: Level IV, therapeutic study. See Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".