Great debates in trauma biomechanics
Bibliographic record
Abstract
At the 2021 annual meeting of the Orthopaedic Trauma Association, the Basic Science Focus Forum hosted its first ever debate-style symposium focused on biomechanics and fracture repair. The 3 subjects of debate were "Mechanics versus Biology-Which is 'More Important' to Consider?" "Locked Plate versus Forward Dynamization versus Reverse Dynamization-Which Way Should I Go?" and "Sawbones versus Cadaver Models-What Should I Believe Most?" These debates were held because fracture healing is a highly organized synergistic response between biological factors and the local mechanical environment. Multiple studies have demonstrated that both factors play roles in governing bone healing responses, and the causal relationships between the 2 remain unclear. The lack of clarity in this space has led to a spectrum of research with the common goal of helping surgeons make good decisions. Before reading further, the reader should understand that the questions posed in the debate titles are unanswerable and might represent a false choice. Instead, the reader should appreciate that the debates were held to gain a more thorough understanding of these topics based on the current state of the art of experimental and clinical studies, by using an engaging and thought-provoking format.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".