Feasibility and value of non-locking retrograde nail vs. locking retrograde nail in fixation of distal third femoral shaft fractures: radiographic, bone densitometry and clinical outcome assessments
Bibliographic record
Abstract
<p><strong>Aim<br /></strong> Distal femoral shaft fractures are characterized by increasing incidence and complexity and are still considered a challenging problem. No consensus on best surgical option has been achieved. The aim of this study is to investigate mineral bone densitometry, radiographic and clinical outcomes of locking retrograde intramedullary nailing (LRN) and non-locking retrograde intramedullary nailing (NLRN) regarding surgical treatment of distal femoral shaft fractures in adults based on the hypothesis that there is no statistical difference among the results of both surgical options. <br /><strong>Methods<br /></strong> Retrospective study: 30 patients divided into 2 groups (Group 1 LRN, Group 2 NLRN). Average age was 42.67±18.32 for Group 1 and 44.27±15.11 for Group 2 (range of age 18-65 for both groups). Gender ratio (male:female) was 2.75 (11:4) for both groups. AO Classification, Non Union Scoring System (NUSS) and Radiographic Union Score Hip (RUSH), Visual Analogic Score (VAS), Dexa scans, plain radiographs were used. Evaluation endpoint: 12 months after surgery. <br /><strong>Results<br /></strong> No statistical difference was obtained in terms of surgery time, transfusions or wound healing. There were similar results regarding average time of bone healing, RUSH scores, VAS, regression between RUSH and VAS, average correlation clinical-radiographic results and patients outcomes. Only one patient of LRN group had reduction of mineral bone densitometry values. <br /><strong>Conclusion<br /></strong>No statistical difference in terms of radiographic, bone densitometry and clinical outcomes among LNR and NLNR for the treatment of distal femur fractures was found. The presence of no statistical difference regarding radiological findings is the main factor supporting our hypothesis given their strong objectivity.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".