Operative Treatment of Extra-Articular Proximal Tibial Fractures
Bibliographic record
Abstract
BACKGROUND: Extra-articular proximal tibial fractures are often the result of high-energy trauma with displacement and comminution. Most authors agree that operative management of these fractures is warranted to optimize patient outcomes. It is unclear, however, which surgical option (plate, nail, external fixator, or combination) is preferable. (1-17) OBJECTIVE: To evaluate the effect of alternative operative techniques in the management of extra-articular proximal third tibial fractures on rates of nonunion, malunion, infection, compartment syndrome, and implant failure. HIGHEST AVAILABLE EVIDENCE: 1. Intramedullary nail (level 3 prospective case series). 2. Plates (level 3 prospective case series). 3. External fixator (level 4 case series). STUDY IDENTIFICATION: 1. Cochrane Database: 0 articles. 2. PubMed Search: Proximal tibial fractures: 568 hits; And external fixation: 69 hits; And plate: 103 hits; And intramedullary nail: 77 hits. Clinical queries search: proximal tibial fracture (specificity/therapy): 1 hit; proximal tibial fracture (sensitivity): 24 hits. Systematic reviews: 3 hits (none relevant). 3. OTA website abstract database: 8 abstracts. Total number potentially relevant articles: 29 (following review of all study titles and abstracts). Total number included after review: 17 (1-17) .
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".