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
Bibliographic record
Abstract
# 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.029 | 0.047 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.014 |
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".