JBJS Open Access 2024 Award Winners and Journal Update
Bibliographic record
Abstract
JBJS Open Access (OA) is pleased to continue our tradition of granting 2 annual awards—one for the best scientific article and 1 for the best AOA Critical Issues in Education article—to acknowledge and celebrate the impactful work of our authors. Since its launch in 2016, JBJS OA has seen a steady increase in manuscript submissions and global readership. In 2024, we received 253 submissions and published 42 scientific articles and 38 AOA Critical Issues in Education articles. Last year was also JBJS OA’s first year accepting review articles for publication, of which we published 15. We are happy to share that the number of article downloads and page views also continues to increase. In addition, JBJS OA received its first impact factor of 2.3 in 2024, a milestone which will no doubt improve the visibility and prominence of the journal. We deeply appreciate the ongoing support from our readers and authors and hope that you find JBJS OA articles to be engaging, insightful, and pertinent to the evolving landscape of orthopaedic surgery and education. We would also like to extend our sincere thanks to the members of our international Editorial Board for their diligent assessment of manuscripts and seamless coordination of the editorial process, which ensures the exceptional quality of our journal content. From among the manuscripts published in 2024, we have selected the award winners based on the following criteria: Broad interest across the spectrum of bone and joint surgery Rigorous methodology, a strong message, and useful images or illustrations The potential to change practice and/or improve orthopaedic education Findings of international relevance Congratulations to our 2024 winners: Gitajn et al., “Deep Surgical Site Infection after Fracture Has a Profound Effect on Functional Outcomes,” published on January 9, 2024 Tarapore et al., “The Impact of Signaling on the 2022 to 2023 Orthopaedic Residency Application Cycle: A Survey of Incoming Residents,” published on February 15, 2024 We wish everyone a productive and fulfilling 2025.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.198 | 0.143 |
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".