Insulin‐Like Growth Factor <scp>I</scp> Inhibits Alveolar Bone Loss Following Tooth Extraction in Rats
Bibliographic record
Abstract
BACKGROUND: Alveolar bone is often lost after tooth extraction. Few studies have assessed the longitudinal changes in bone volume that occur within these extraction sites. PURPOSE: To investigate the longitudinal morphological changes in extraction sockets following sustained continuous subcutaneous infusion of human recombinant insulin-like growth factor I (IGF-I). MATERIALS AND METHODS: Fourteen rats were subjected to right mandibular first molar extraction. Experimental rats (n = 7) received a continuous subcutaneous infusion of human recombinant IGF-I (320 mg/day) for 3 weeks by osmotic minipump. Control animals were treated with saline via the same method (n = 7). All rats were then housed for an additional 3 weeks. Micro-CT scanning was performed immediately after tooth extraction and at 1, 2, 3, and 6 weeks after extraction. RESULTS: New bone formation was markedly higher in the IGF-I-treated group as compared with the control group. The loss in alveolar ridge height in the IGF-I group was significantly lower than that in the control group at each time point after extraction on the buccal side and at 2, 3, and 6 weeks on the lingual side. CONCLUSIONS: IGF-I treatment increases the volume of newly formed bone and reduces the loss in alveolar ridge height following tooth extraction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".