C-CASE 2024: Surgical Education Through Innovation01. A 25-year retrospective of Canadian plastic surgery research and its influence: a thorough bibliometric study02. Evaluating knowledge translation applications of a Canadian surgical education app among Saudi Arabian medical trainees03. An educational podcast for trainees to learn about plastic surgery training in Canada — “Doctority Canada: Plastic Surgery.”04. Educational landscape and perspectives on interventional neuroradiology training in residency: a scoping review05. Moving toward collaboration: introducing a pan-Canadian virtual patient initiative06. From classroom to the operating room: equipping medical students for surgical clerkship success through a skills-based workshop07. Participants’ initial reactions and appreciation of ExploreMD, a medical career exploration event in the region of Outaouais, Quebec08. Shoulder dislocation: Health care professionals produce higher-quality content on TikTok09. Weaving our narrative: perspectives from hijab-wearing learners in the operating room10. The impact of extended reality simulators on ophthalmic surgical training and performance: a systematic review and meta-analysis of 17 623 eyes11. Impact of a new bilingual online career orientation tool on medical students12. Examining the utility of LearnENT in undergraduate medical education13. Evaluating the current teaching practices for robotic-assisted surgery training during residency in different surgical programs across Canada14. Early exposure to neurosurgery: assessment of perceptions, expectations, mentorship, representation, and competence on medical student interest in neurosurgery15. Digital scribes in medical education: balancing innovation with skill development across educational levels16. A blueprint for near-peer medical student anatomy tutoring17. Enhancing productivity in medical education research groups: a scoping review18. Generative artificial intelligence in plastic surgery medical education: a quality improvement scoping review19. An in-depth exploration of the entrustable professional activity (EPA) assessment–related emotions of residents and faculty across specialties20. Outcomes of a competency-based microlearning mobile application for surgical residents21. Enhancing clinical clerk surgical preparedness using microlearning modules22. From model to mastery: a randomized study on the effect of anatomic model building on medical students’ surgical skills23. From textbooks to headsets: the role of virtual reality in improving medical students’ understanding of liver anatomy24. The Symposium of Medical Student Leadership Development: an initiative to develop future surgical and health care leaders25. Effectiveness of near-peer teaching and experiential learning of casting and splinting: a medical student’s perspective26. Development of a novel simulation-based mastery learning course for extracorporeal membrane oxygenation (ECMO) cannulation27. Barriers to Black medical students and residents pursuing and completing surgical residency in Canada: a qualitative analysis28. Introduction of an academic half-day teaching for clinical clerks in surgery: a pilot study29. Efficiency of verbal intelligent tutor instruction in neurosurgical simulation: a randomized controlled trial30. Creation of a novel mindfulness-based cognitive therapy curriculum for surgical trainees: Mindfulness Integration in Surgical Training (MIST) 231. Comparison of ChatGPT and Gemini in responding to pediatric surgery clinical scenarios32. Examining the quality and quantity of verbal feedback in the operating room: a multispecialty study33. Influence of pig eye suturing on medical students’ perception of ophthalmic surgery in community medicine34. The use of different peer feedback frequencies in the acquisition of surgical skills using a decentralized model of simulation
Notice bibliographique
Résumé
# 01. A 25-year retrospective of Canadian plastic surgery research and its influence: a thorough bibliometric study {#article-title-2} Bibliometric analysis is used to assess and interpret the academic output and impact within a specific field. We aimed to measure the quantity and quality of
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 | 0,015 | 0,136 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,029 | 0,047 |
| Études des sciences et des technologies | 0,007 | 0,002 |
| Communication savante | 0,009 | 0,006 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,086 | 0,014 |
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 ».