VIS-IT: Visualizing the Injured Tibia—A Cadaveric Study of Limb Positioning for Posterolateral Tibial Plateau Fracture Visualization
Bibliographic record
Abstract
Posterolateral tibial plateau (PLTP) fractures are often associated with anterior cruciate ligament (ACL) incompetence, such as tibial eminence fractures. Both occur from a pivot shift like mechanism. Malreductions of the tibial plateau most frequently occur in the posterolateral quadrant. Acquiring adequate intraoperative visualization of the PLTP poses a challenge. We hypothesized that visualization of PLTP could be improved by positioning the knee at 110 degrees of flexion with the addition of a varus anterolateral rotatory vector. This position and maneuver take advantage of both the nonisometric nature of the lateral soft tissues and, when present, ACL incompetence. In this cadaveric study, we digitally quantified the percentage of the lateral tibial plateau visualized under different conditions after performing an anterolateral surgical approach with submeniscal arthrotomy. Four conditions were assessed for articular visualization: (1) 30 degrees of knee flexion, (2) 110 degrees of knee flexion, (3) 110-degrees of knee flexion plus varus anterolateral rotatory vector, (4) 110-degrees of knee flexion plus varus anterolateral rotatory vector with ACL sacrifice (ACL incompetence model). In the ACL competent models, maximal lateral tibial plateau exposure was obtained with the knee positioned at 110 degrees of flexion with a varus anterolateral rotatory vector (58.2%, range: 52.9-63.4%). Articular visualization was further improved with the ACL incompetent model (82.4%, range: 77.1-87.7%), modeling a tibial eminence fracture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".