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 the acute shortening technique (AST) in the treatment of infected tibial bone defects.Methods: A literature survey was conducted by searching the PubMed, Web of Science, Cochrane Library, and Embase databases together with the China National Knowledge Infrastructure (CNKI) and the Wanfang database for articles published up to 9 August 2019. The Newcastle-Ottawa scale (NOS) was adapted to evaluate the bias and risks in each eligible study. The data of the 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) was used for the binary variables and standard mean difference (SMD) for continuous variables. Each variable included its 95% confidence interval (CI).Results: Five studies, including a total of 199 patients, were included in the meta-analysis. Statistical significance was observed in the 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 complications (RR = 0.76, 95% CI: 0.41, 1.39, P = 0.37).Conclusions: AST is preferred from the aspect of minimising the treatment period, whereas 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.Trial registration: PROSPERO CRD42019133659
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.057 |
| 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".