Anti‐<scp>VEGF</scp>s hinder bone healing and implant osseointegration in rat tibiae
Bibliographic record
Abstract
AIM: To assess the effect of anti-vascular endothelial growth factors (VEGF) on bone healing (defect volume) and implant osseointegration (bone-implant contact per cent) in rat tibia. MATERIALS AND METHODS: In Sprague-Dawley rats (n = 36), a unicortical defect was created in the right tibia and a titanium implant was placed in the left tibia of each rat. Rats were assigned into three groups and received either anti-vascular endothelial growth factor neutralizing antibody, Ranibizumab or saline (control). Two weeks following surgery, rats were euthanized and bone samples were retrieved. Bone healing and osseointegration were assessed using micro-CT and histomorphometry. One-way anova followed by the Tukey's test was used for data analyses. RESULTS: The volume of the bone defects in the anti-VEGF group (2.48 ± 0.33 mm(3) ) was larger (p = 0.026) than in the controls (2.11 ± 0.36 mm(3) ) as measured by μ-CT. Bone-implant contact percent in the anti-VEGF (19.9 ± 9.4%) and Ranibizumab (21.7 ± 9.2%) groups were lower (p < 0.00) than in the control group (41.8 ± 12.4%). CONCLUSIONS: The results of this study suggest that drugs that inhibit the activity of vascular endothelial growth factor (i.e. anti-VEGF) may hinder bone healing and implant osseointegration in rat tibiae.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".