Efficacy of fibula fixation in the early treatment of Osteonecrosis of the femoral head and its effects on local microcirculation, articular surface collapse, joint pain and function.
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to investigate the efficacy of fibula fixation in the early treatment of osteonecrosis of the femoral head (ONFH). METHODS: 130 patients with ONFH were selected and randomly divided into control group and observation group. Patients in the control group received core decompression treatment, while patients in the observation group received fibula fixation. The local microcirculation was observed by dynamic contrast-enhanced MRI, necrotic volume was measured using MRI, articular surface collapse by using X-ray, McGill pain questionnaire was used to understand and compared the joint pain condition. RESULTS: Τhe total effective rate of the observation group was significantly higher than that of the control group (p⟨0.05). Necrosis volume of observation group was significantly smaller than that of control group (p⟨0.05). Degree of joint pain was significantly lower in observation group than in control (p⟨0.05). Harris scores were higher in observation group than in control group (p⟨0.05). All life scores of observation were significantly higher than those of control group (p⟨0.05). CONCLUSION: fibula fixation seems to be not superior to core decompression n preventing articular surface collapse, but it can effectively relieve the joint pain in patients with early ONFH.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".