Radiographic Outcomes of Cable-Plate versus Cable-Grip Fixation in Periprosthetic Fractures of the Proximal Femur
Bibliographic record
Abstract
BACKGROUND: Newer generation cable-plate designs are commonly used for periprosthetic proximal femur fractures; however, comparisons relative to cable-grips remain limited. The aim of this study was to compare radiographic healing rates of cable-plate versus cable-grip fixation for periprosthetic proximal femur fractures. PATIENTS AND METHODS: Consecutive patients with an acute or chronic Vancouver A, B1, or B2 periprosthetic proximal femur fracture undergoing trochanteric fixation with a cable-plate (n = 46 cases) or cable-grip (n = 24 cases) system were identified retrospectively from a single-centre hospital database (mean follow-up 28 months [range 6-89 months]). Demographics, radiographic fracture healing, and complications were compared between the 2 groups. Radiographic union rates were not different between the cable-grip versus cable-plate group (67% vs. 76% respectively; p = 0.4). Healing rates of greater trochanteric fractures alone were not different between the cable-plate versus cable-grip groups (75% vs. 71% respectively; p = 0.38). The cable-plates were used for a more diverse range of fracture patterns relative to the cable-grips. RESULTS: An increased number of cables was associated with radiographic healing (odds ratio 14 [95% confidence interval 2-64]; p = 0.01), and body mass index had a negative correlation with radiographic healing (odds ratio -0.4 [95% confidence interval 0.5-0.9]. CONCLUSIONS: Similar rates of periprosthetic fracture healing were seen using a cable-grip versus cable-plate system; however, the cable-plate system could be used for a more diverse range of fracture patterns.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".