Distraction osteogenesis in the treatment of long bone defects of the lower limbs
Bibliographic record
Abstract
We carried out a systematic review of the literature to evaluate the evidence regarding the clinical results of the Ilizarov method in the treatment of long bone defects of the lower limbs. Only 37 reports (three non-randomised comparative studies, one prospective study and 33 case-series) met our inclusion criteria. Although several studies were unsatisfactory in terms of statistical heterogeneity, our analysis appears to show that the Ilizarov method of distraction osteogenesis significantly reduced the risk of deep infection in infected osseous lesions (risk ratio 0.14 (95% confidence interval (CI) 0.10 to 0.20), p < 0.001). However, there was a rate of re-fracture of 5% (95% CI 3 to 7), with a rate of neurovascular complications of 2.2% (95% CI 0.3 to 4) and an amputation rate of 2.9% (95% CI 1.4 to 4.4).The data was generally not statistically heterogeneous. Where tibial defects were > 8 cm, the risk of re-fracture increased (odds ratio 3.7 (95% CI 1.1 to 12.5), p = 0.036). The technique is demanding for patients, illustrated by the voluntary amputation rate of 1.6% (95% CI 0 to 3.1), which underlines the need for careful patient selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".