Posterior malleolus exposure
Bibliographic record
Abstract
Abstract Objectives: Posterior malleolus (PM) fractures are common in rotational ankle injuries, tibial plafond fractures, and distal third tibia fractures. Surgical indications continue to evolve as we improve our understanding of ankle and syndesmotic stability. These fractures remain technically challenging with respect to both exposure and fixation. Our biomechanical cadaveric study compared posterolateral versus modified posteromedial surgical approaches to define the following: maximal surface area exposed, and maximal screw trajectory obtainable for fixation. Methods: Twelve fresh-frozen cadaver limbs were thawed at room temperature. Posterolateral and modified posteromedial approaches were performed on each limb. Margins of exposure were marked. A 2.5 mm drill was advanced at the extreme medial and lateral extents of each exposure, standardized at 1 cm proximal to the joint line and perpendicular to the bone. Computed tomography (CT) scans were performed to identify the maximal trajectory. Limbs were stripped of soft tissue, and the exposed bony surface area was measured using a validated laser surface-scanning technique. Results: The modified posteromedial approach allowed for a larger exposed surface area compared to the posterolateral exposure (median 99% vs 64%, respectively; P < .05). The modified posteromedial approach allowed for instrumentation of up to a median of 77% of the posterior distal tibia as opposed to 46% through the posterolateral approach (P < .05). Conclusion: The modified posteromedial approach allowed for increased exposure and wider access for instrumentation of the PM when compared to the posterolateral approach. We advocate use of this approach when addressing complex PM fractures, in particular the Haraguchi type 2 fracture pattern.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".