Biomaterial‐Stabilized Soft Tissue Healing for Healing of Critical‐Sized Bone Defects: the Masquelet Technique
Bibliographic record
Abstract
Critical-sized bone defects present a significant burden to the medical community due to their challenging treatment. However, a successful limb-salvaging technique, the Masquelet Technique (MT), has significantly improved the prognosis of many segmental bone defects in helping to restore form and function. Although the Masquelet Technique has proven to be clinically effective, the physiology of the healing it induces is not well understood. Multiple modifiable factors have been implicated by various surgical and research teams, but no single factor has been proven to be critical to the success of the Masquelet Technique. In this review the most recent clinical and experimental evidence that supports and helps to decipher the traditional Masquelet, as well as the modifiable factors and their effect on the success of the technique are discussed. In addition, future developments for the integration of the traditional Masquelet Technique with the use of alternative biomaterials to increase the effectiveness and expand the clinical applicability of the Masquelet Technique are reviewed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.001 |
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".