A survey on idiopathic congenital talipes equinovarus (ICTEV) managed by the Ponseti technique at Mulago Hospital - Uganda
Bibliographic record
Abstract
INTRODUCTION: Ponseti technique is the treatment of choice for idiopathic congential talipes equino varus (ICTEV) since 1950s with excellent treatment outcomes reported worldwide. However, despite the popularity of this technique, Uganda adapted it as a treatment modality for ICTEV in May 2005. Since then, the effectiveness of delivered Ponseti care to children with this very common orthopaedic deformity under the supervision of an orthopaedic surgeon was unknown. The implication of this undertaking was that, satisfactory outcomes would then support the Ministry of Health (MOH)-Uganda´s decision to embrace this mode of treatment and if the outcomes were unsatisfactory, MOH would then consider a policy revision in this regard. To assess the midterm treatment outcomes of children with ICTEV who had been enrolled for treatment at Mulago National Referral Hospital in the period of 2006-2009. METHODS: in November/December 2013, a cross-sectional study was conducted to assess the treatment outcomes of 68 feet of 45 children using the designed questionnaire and the PBS score; a pilot study of 10 neonates was performed prior to research. A good treatment outcome meant having a foot or feet that did not require any major or minor surgery. RESULTS: forty-five (45) children with 68 ICTEV feet were evaluated; males 29 (64.4%) and 16 (35.6%) females with a mean age of 73.22 months (SD 11.364, range 48-96 months). Among the feet assessed, 46 (68%) had good to excellent outcomes while 22 (32%) had a relapse of moderate and severe deformity. Good functionality was seen in 61.8% out of which, 69% and 55.9% had no limitation in walking or running respectively. CONCLUSION: Ponseti treatment technique in children with ICTEV under the care of predominantly orthopaedic officers with some supervision from orthopaedic surgeons had fair to good midterm outcomes even in low resource settings like Uganda. Public health approach should be embraced in the management of clubfoot in Uganda by enhancing adequate comprehensive support supervision and establishment of reliable institutionalized systems for patient follow up which will lead to early detection and treatment of relapsed ICTEV cases or neglected clubfeet in the communities.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".