The induced membrane technique for the management of long bone defects
Bibliographic record
Abstract
Aims The purpose of this study was to: review the efficacy of the induced membrane technique (IMT), also known as the Masquelet technique; and investigate the relationship between patient factors and technique variations on the outcomes of the IMT. Methods A systematic search was performed in CINAHL, The Cochrane Library, Embase, Ovid MEDLINE, and PubMed. We included articles from 1 January 1980 to 30 September 2019. Studies with a minimum sample size of five cases, where the IMT was performed primarily in adult patients (≥ 18 years old), in a long bone were included. Multivariate regression models were performed on patient-level data to determine variables associated with nonunion, postoperative infection, and the need for additional procedures. Results A total of 48 studies were included, with 1,386 cases treated with the IMT. Patients had a mean age of 40.7 years (4 to 88), and the mean defect size was 5.9 cm (0.5 to 26). In total, 82.3% of cases achieved union after the index second stage procedure. The mean time to union was 6.6 months (1.4 to 58.7) after the second stage. Our multivariate analysis of 450 individual patients showed that the odds of developing a nonunion were significantly increased in those with preoperative infection. Patients with tibial defects, and those with larger defects, were at significantly higher odds of developing a postoperative infection. Our analysis also demonstrated a trend towards the inclusion of antibiotics in the cement spacer having a protective effect against the need for additional procedures. Conclusion The IMT is an effective management strategy for complex segmental bone defects. Standardized reporting of individual patient data or larger prospective trials is required to determine the optimal implementation of this technique. This is the most comprehensive review of the IMT, and the first to compile individual patient data and use regression models to determine predictors of outcomes. Cite this article: Bone Joint J 2020;102-B(12):1723–1734.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".