Open Reduction and Internal Fixation Versus Acute Arthroplasty for the Management of Common Extremity Injuries: Evidence-Based Decision Making.
Bibliographic record
Abstract
A considerable burden of disease is associated with the management of periarticular fractures. Increasingly, evidence-based medicine is used to define the standard of clinical care. The role of internal fixation in the management of periarticular fractures, particularly in elderly patients, has been questioned. Currently available evidence-based medicine studies may help surgeons decide whether open reduction and internal fixation or arthroplasty is appropriate for the management of common periarticular injuries. The management of periarticular injuries about the shoulder, elbow, hip, and knee is controversial. The long-term outcomes of patients with a periarticular upper or lower extremity injury who undergo open reduction and internal fixation are limited by high complication and revision surgery rates and poor functional outcomes. Despite evidence-based medicine decision making and the substantial number of prospective clinical trials available in the literature, a lack of consensus with regard to best practices for the surgical management of periarticular injuries exists. This lack of consensus has substantial implications given that proximal humerus, elbow, hip, and knee fractures are common and that the role of acute arthroplasty in the management of periarticular injuries is changing.
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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 | 0.032 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".