The Otto Aufranc Award: Demineralized Bone Matrix Around Porous Implants Promotes Rapid Gap Healing and Bone Ingrowth
Bibliographic record
Abstract
BACKGROUND: Noncemented revision arthroplasty is often complicated by the presence of bone implant gaps that reduce initial stability and biologic fixation. Demineralized bone matrix has osteoinductive properties and therefore the potential to enhance gap healing and porous implant fixation. QUESTIONS/PURPOSES: We determined at what times and to what extent demineralized bone matrix promotes gap healing and bone ingrowth around a porous implant. METHODS: We inserted porous titanium implants into the proximal metaphyses of canine femora and humeri, with an initial 3-mm gap between host cancellous bone and implants. We left the gaps empty (control; n = 12) or filled them with either demineralized bone matrix (n = 6) or devitalized demineralized bone matrix (negative control; n = 6) and left them in situ for 4 or 12 weeks. We quantified volume healing of the gap with new bone using three-dimensional micro-CT scanning and quantified apposition and ingrowth using backscattered scanning electron microscopy. RESULTS: The density of bone inside gaps filled with demineralized bone matrix reached 64% and 93% of surrounding bone density by 4 and 12 weeks, respectively. Compared with empty controls and negative controls at 4 and 12 weeks, gap healing using demineralized bone matrix was two to three times greater and bone ingrowth and apposition were up to 15 times greater. CONCLUSIONS: Demineralized bone matrix promotes rapid bone ingrowth and gap healing around porous implants. CLINICAL RELEVANCE: Demineralized bone matrix has potential for enhancing implant fixation in revision arthroplasty.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.020 |
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".