Education and Balance at Its Best—The 2023 AANA Traveling Fellowship Was the Adventure of a Lifetime: <i>Nothing Beats Having Fun</i>
Bibliographic record
Abstract
We remain honored and humbled to have been 2023 Arthroscopy Association of North America (AANA) Traveling Fellows. To borrow a previous Traveling Fellow’s tweet: “Every fellowship class thinks their year was the best… but ours was.” Our godfather and past president of AANA, Nicholas Sgaglione, M.D., appropriately from New York, was the quintessential godfather. Spanning the U.S. coast to coast and Canada, the fellowship class comprised of Albert Gee, M.D., Associate Professor and Chief of Sports Medicine at University of Washington in Seattle and Team Physician University of Washington; Catherine Hui, M.D., Associate Clinical Professor at University of Alberta and Knee Team lead at Glen Sather Sports Medicine Clinic; Theodore Shybut, M.D., faculty at Southern California Orthopedic Institute and Team Orthopedic Surgeon for College of the Canyons; and Peter S.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.066 | 0.036 |
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".