“I Stumbled over the Ponseti Method almost by Accident”: In Conversation with Dr Shafique Pirani on His Adventures into Global Sustainable Clubfoot Care
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,052 | 0,025 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».