The induced membrane technique in animal models: a systematic review
Bibliographic record
Abstract
Objectives: The induced membrane technique (IMT) is a 2-stage surgical approach that has become increasingly popular to manage bone defects. Preclinical investigations have been conducted to better understand and define several aspects of this technique. This review summarizes the literature regarding the IMT performed in animal models and identifies potential future directions. Data Sources: Biosis Citation Index, Ovid Embase, and Ovid MEDLINE databases were searched from inception up to June 23, 2021 for articles related to the IMT. Study Selection: Animal studies involving the use of the IMT for segmental defects in long bones were selected. Only full-length original research articles published in English or French were included. Data Extraction: Two authors extracted the data from the selected studies and a third author verified the accuracy of the information. Data Synthesis: Information concerning the animal model, the surgical procedures, and the outcome measures were recorded for each study and compiled. Conclusions: Forty-seven studies were included in this review. Twenty-nine studies (62%) performed both stages of the technique, but only 8 (17%) reported on radiographic union rates explicitly and 5 (11%) included biomechanical testing. A large proportion of the preclinical literature on the IMT has failed to report on radiographic union as an outcome. While studies reporting membrane properties are valuable, they may not provide information that translates into clinical practice or further clinical research if the ultimate outcome of bony healing is not considered. Future animal studies of the IMT should consider this in their study design.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".