Intramedullary Versus Extramedullary Fixation for Unstable Intertrochanteric Fractures
Bibliographic record
Abstract
BACKGROUND: The use of intramedullary devices for the management of intertrochanteric fractures has steadily increased without good evidence of their clinical efficacy. This prospective randomized multicenter study was designed to compare the clinical and radiographic outcomes of patients who had been treated with a traditional extramedullary hip screw for an unstable (AO/OTA 31-A2) intertrochanteric hip fracture with those of patients who had been treated with the newer intramedullary device for the same injury. METHODS: The Lower Extremity Measure (LEM) was used as the primary hip-specific outcome tool. The Functional Independence Measure (FIM), the timed "Up & Go" (TUG) test, as well as a timed two-minute walk test were used as secondary clinical outcome tools. Specific radiographic parameters were collected to assess for fracture movement, heterotopic ossification, and implant failure. RESULTS: No significant differences were noted between the intramedullary and extramedullary treatment arms with regard to either the primary or the secondary clinical outcome tools. The radiographic parameters favored the intramedullary treatment arm, which had less femoral neck shortening. CONCLUSIONS: While the use of the intramedullary devices led to better radiographic outcomes in this study, this did not translate to improved functional outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".