Bone transport versus acute shortening for the management of infected tibial bone defects: a meta-analysis
Bibliographic record
Abstract
Abstract Background The treatment for infected tibial bone defects can be a great challenge for the orthopaedic surgeon. This meta-analysis was conducted to compare the efficacy and safety between bone transport (BT) and acute shortening technique (AST) in the treatment of infected tibial bone defects.Materials and Methods A literature survey was conducted by searching the PubMed, Web of Science, Cochrane Library, Embase together with China National Knowledge Infrastructure (CNKI), and Wanfang database for articles published as of August 9, 2019. NOS (Newcastle-Ottawa scale) and Cochrane's risk of bias tool were adapted to evaluated the bias and risk of each eligible study. The data of external fixation index (EFI), bone grafting, bone and functional results, complications, bone union time and characteristics of participants were extracted. RevMan V.5.3 was used to perform relevant statistical analyses. Relative risk (RR) were used for the binary variables and standard mean difference (SMD) for continuous variable. Each variable included its 95% confidence interval (CI).Results 5 studies, including a total of 199 patients, were included in the meta-analysis. Statistical significance was observed in EFI (SMD = 0.63,95% CI:0.25,1.01,P=0.001) and Bone grafting (RR = 0.26,95%CI:0.15,0.46,P<0.00001), however, no significance was observed in bone union time (SMD = -0.02, 95% CI: -0.39, 0.35, P=0.92), bone results (RR = 0.97,95%CI:0.91,1.04,P=0.41),functional results (RR = 0.96,95%CI:0.86,1.08,P=0.50) and complication (RR = 0.76,95%CI:0.41,1.39,P=0.37).Conclusions AST is preferred on the aspect of minimizing treatment period, while BT is superior to AST for reducing bone grafting. Due to the limited number of trials, The meaning of this conclusion should be taken with caution for infected tibial bone defects.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.058 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".