Basic Science Focus Forum supplement of the Orthopaedic Trauma Association 2022
Bibliographic record
Abstract
It is my pleasure to introduce the Basic Science Focus Forum supplement for 2022. Last year we were thrilled to return to a full in-person meeting in Fort Worth, TX, including a full agenda for the Basic Science Focus Forum. The Basic Science Committee put together an excellent slate of cutting edge symposia to complement the high-level preclinical research presented. This supplement contains review articles summarizing each of these symposia. These articles were authored by our symposia speakers who are leading experts in their fields internationally. I am very proud of this compilation of articles and the excellent work done by the members of the Basic Science Committee. These articles review and summarize the latest basic science evidence in important areas of orthopaedic trauma including: debates in biomechanics, orthopaedic infections, clinical trial design and analysis strategies, orthobiologics for fracture healing, and post-traumatic osteoarthritis. In addition, 2 highly ranked original research papers from the meeting were selected for inclusion in this supplement. These review articles were written with the earnest intent of “bridging the gap” between the latest basic science research and clinical practice in orthopaedic trauma. I sincerely hope that you enjoy reading them as much as I did.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.356 | 0.198 |
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".