Clubfoot Care in Low‐Income and Middle‐Income Countries: From Clinical Innovation to a Public Health Program
Bibliographic record
Abstract
BACKGROUND: Clubfoot occurs in nearly 1 in every 1,000 live births worldwide, representing a significant burden of disease. In high-income countries, an evidence-based treatment protocol utilizing sequential casting was pioneered by Ponseti and has resulted in excellent outcomes among children treated for this condition. However, treatment methods and results of treatment vary greatly across low- and middle-income countries (LMICs). Our goal was to create a framework for understanding how effective programs that treat clubfoot in LMICs choose and organize their activities. METHODS: A systematic literature review was conducted using the keywords "developing countries" and "clubfoot." A public health analysis model known as the Care Delivery Value Chain (CDVC) was applied to discover public health practices that would optimize value over the entire course of a patient's life. RESULTS: The literature review yielded 32 unique results, seven of which met our inclusion and exclusion criteria. Review of the bibliographies yielded two additional papers for a total of nine papers. We identified seven vital steps in the clubfoot cycle of care and constructed a CDVC. CONCLUSIONS: The analysis of this CDVC model suggests six best practices that are essential to successfully scaling up clubfoot treatment programs and ensuring excellent clinical outcomes: (1) diagnosing clubfoot early; (2) organizing high-volume Ponseti casting centers; (3) using nonphysician health workers; (4) engaging families in care; (5) addressing barriers to access; (6) providing follow-up in the patient's community. These practices must be adapted to each context. Applying them will optimize outcomes when designing public health programs that deliver clubfoot care in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".