Bone grafting augmentation choices in complex proximal humerus fractures: A systematic review
Bibliographic record
Abstract
Objective: To systematically identify and evaluate different bone graft augmentation techniques in the operative treatment of complex proximal humerus fractures. Methods: Four databases were searched from 1970 to February 2023 for Level I to IV English studies that investigated outcomes of different bone augments in the primary surgical fixation of proximal humerus fractures. The JBI critical appraisal checklist, methodological index for non-randomized studies and cochrane risk of bias tool were used to assess study quality. Descriptive statistics including weighted means are presented where applicable. Methods: Thirty-three articles including 964 patients met the inclusion. Seven bone augments were identified, including fibular strut allograft (693 patients across 21 studies), femoral head allograft (84 patients across 4 studies), iliac crest allograft (54 patients across 3 studies), iliac crest autograft (94 patients across 5 studies), humeral endosteal allograft (6 patients in 1 single study), unspecified cancellous allograft (28 patients in 1 single study) and distal clavicle autograft (3 patients in 1 single study). Mean patient age was 67.1 years, with female patients comprising 65.2 %. Fracture union rates were similar between groups, with an average of 99.6 %. The average Constant Murley Score (CMS) was not reported in the humeral endosteal allograft or the distal clavicle autograft group but was 81.8 (fibular strut allograft), 79.1 (femoral head allograft), 76.8 (iliac crest allograft), 77.7 (iliac crest autograft), and 81.5 (unspecified cancellous allograft) in the remaining groups. Revision surgery was required in 16.7 % of patients receiving humeral endosteal allograft, 7 % of patients with femoral head allograft, 2 % of iliac crest autografts and 1.9 % in the fibular allograft group. Reported complications included avascular necrosis, hardware complications and loss of reduction. Conclusion: Bone graft augmentation is an effective adjunct to open reduction internal fixation of complex proximal humerus fractures. Fibular strut allograft is the most common bone graft augment used. Majority of patients treated with bone graft augmentation achieved bony union (83%-100 %) and average CMS scores at final follow-up were similar between graft types (76-82). However, no conclusive data suggests superiority of one bone graft type over another. Future studies should aim to compare the outcomes amongst graft types.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".