1016 Qualitative Account of an Arthroplasty Elective Experience at the Hospital for Special Surgery in New York
Bibliographic record
Abstract
Abstract Aim Junior doctors applying for Core Surgical Training are awarded points for completing a surgical elective. As a final year medical student in 2023, I travelled to The Hospital for Special Surgery (HSS) in New York, at the Complex Joint Reconstruction Centre for four-weeks. I aim to showcase the benefits of travelling abroad for UK medical students interested in orthopaedic surgery. Method This is a qualitative account of my experiences working with attendings and research fellows in the Complex Joint Reconstruction Centre at HSS. Results I attended clinics and scrubbed into theatres using a variety of innovative robotic technology in knee arthroplasty, including robotic-assisted knee arthroplasty (Mako Smart Robotics™ and ROSA® Knee System) and computer-assisted technology (Intellijoint KNEE® Surgical Inc, Kitchener, Ontario). I was actively involved in departmental research meetings, drafting two case reports in cementing techniques to improve Varus-Valgus alignment in Total Knee Arthroplasty (TKA). I attended 3D printing workshops which produced custom-implants for complex revision cases. Conclusions An elective at HSS gave me an insightful experience with furthering my understanding of complex orthopaedic conditions, taught by pioneering orthopaedic surgeons in the USA. I broadened my experience in Trauma & Orthopaedics, which has allowed me to make a more informed decision in pursuing this specialty. I encourage medical students and junior doctors to pursue similar experiences to further their passion for orthopaedic surgery.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".