Osteogenic Protein-1 Delivered by Hydroxyapatite-coated Implants Improves Bone Ingrowth in Extracortical Bone Bridging
Bibliographic record
Abstract
BACKGROUND: Extracortical bone bridging for treatment of massive bone loss can improve stability and longevity of massive endoprostheses. Osteogenic protein-1 (OP-1), when used with allograft bone, reportedly improves extracortical bone bridging and bone ingrowth. QUESTIONS/PURPOSES: We asked whether OP-1 delivered by hydroxyapatite (HA) without bone grafting could improve bone ingrowth and bone formation in the context of extracortical bone bridging. METHODS: We implanted unilateral segmental femoral diaphyseal replacement prostheses in 18 dogs (three groups of six dogs). The groups consisted of an HA-coated group augmented with OP-1, an HA-coated group, and a plain porous group. Bone grafting techniques were not used to augment bone formation. The implants were retrieved at 12 weeks for histologic assessment. RESULTS: After removing one specimen owing to a complication, 17 femora were analyzed (six HA-coated augmented with OP-1, five HA-coated, and six plain). We observed better bone ingrowth in the HA-coated OP-1 group than in the plain porous and HA-coated groups, with no difference between the latter two groups. There also was better bone apposition and callus height in the HA-coated OP-1 group than in the plain group but no differences between the HA-coated OP-1 and HA-coated groups or between the HA-coated and plain groups. CONCLUSIONS: OP-1 (2.9 mg) delivered by HA-coated segmental replacement prostheses in this canine extracortical bone bridging model revealed improved bone ingrowth over HA-coated implants without OP-1 or plain porous-coated prostheses.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".