Systemically Administered Human Growth Hormone Improves Initial Implant Stability: An Experimental Study in the Rabbit
Bibliographic record
Abstract
PURPOSE: This study was an investigation to determine whether human growth hormone (hGH) continuously administered to rabbits may improve implant integration in bone. MATERIALS AND METHODS: Thirty-two commercially pure titanium (c.p. Ti) implants were inserted in the tibiae of 16 rabbits. Human growth hormone (0.3 U/kg/d) or sodium chloride (NaCl) was administered by subcutaneous pumps. Insulin-like growth factor-1 (IGF-1) levels in blood were measured. Two biomechanical tests were performed: (1) every second week resonance frequency analysis (RFA) was used to investigate implant stability or stiffness at the interface and, after 8 weeks of follow-up, (2) removal torque (a measure of implant integration and stability) was registered. Further evaluation was performed by dual energy x-ray analysis (DEXA), to evaluate bone mineral density, and histomorphometric analysis of tissue-to-implant integration on undecalcified cut and ground sections. RESULTS: A difference in implant stability was detected with the RFA technique after 2 weeks and 8 weeks in favor of the hGH-treated rabbits. No significant differences were detected with removal torque, DEXA, and histomorphometric measurements. The blood test demonstrated antibody development in the rabbits treated with hGH after 4 weeks. CONCLUSION: Growth hormone has an initial beneficial effect on implant integration; however, owing to rapid antibody formation, this study did not demonstrate whether this effect remains in the long term.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".