“I Stumbled over the Ponseti Method almost by Accident”: In Conversation with Dr Shafique Pirani on His Adventures into Global Sustainable Clubfoot Care
Bibliographic record
Abstract
Introductory Words f r o m dr AlArIc AroojIsIt is a great honour for me to write an introductory foreword to Dr Shafique Pirani's interview by Jolie Leung.Dr Pirani is a global icon in the field of clubfoot and is recognized internationally for his humanitarian work of spreading the Ponseti method across several low-and middle-income countries (LMICs) in Africa and Asia.He is also well-known for his eponymous scoring system for assessing the severity of the clubfoot deformity and his path-breaking MRI studies which beautifully demonstrated gradual correction of tarsal deformations and tarsal joint mal-alignments during serial Ponseti casting.For his global humanitarian work, Dr Pirani has been bestowed with several prestigious awards including the American Academy of Orthopaedic Surgeon's Humanitarian Award, the Pediatric Orthopaedic Society of North America Humanitarian Award, the Canadian Orthopaedic Association's Award for Excellence, the Pediatric Orthopaedic Society of North America's Angie Kuo Award, the University of British Columbia's Impact in the Community Award, and Fraser Health's Above and Beyond Award.Despite these and many other accolades, Dr Pirani is an extremely humble and modest individual and is a constant seeker of new ideas and novel research.He is a true clinician-scientist and an incomparable teacher, who has made it his life's mission to proselytize the conservative treatment of clubfoot throughout the world.Very few are aware of the fact that it was Dr Pirani who popularized the Ponseti method not only in Uganda and sub-Saharan Africa but also in India.A chance meeting with him in 2002, converted many of us (then young) Paediatric Orthopaedists in India from skeptics to staunch acolytes of the Ponseti method.The first-ever Ponseti training workshop in India was conducted by him in Mumbai in 2003 and since then it has become the standard of care all over the country.Dr Pirani shares a close bond with India (he is originally a Gujarati Indian) and has returned to India several times since to share his knowledge and expertise.I am confident the reader will enjoy taking a trip down the memory lane with Dr Pirani and partaking of his reminiscences in this beautiful narrative. AbstrActIn autumn 2019, Dr Alaric Aroojis asked me to interview Dr Shafique Pirani, a well-known teacher and advocate for the Ponseti method, to document his many clubfoot adventures.Dr Aroojis first met and was shown the method by Dr Pirani in 2002, and has since followed his contributions from showing correction of pathology in vivo by MRI to developing the Pirani Score to guide treatment, then teaching the method on every continent and developing public health programs for sustainable clubfoot care, and his current explorations in using technology to improve quality of care.I had the privilege of meeting him several times at his home in the fall of 2019.This is his story extracted from hours of footage.I am honored to tell it.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.052 | 0.025 |
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".