Influence of Insulin-like Growth Factor I on Nerve Regeneration Using Allografts
Bibliographic record
Abstract
Effects of insulin-like growth factor I (IGF I) on peripheral nerve regeneration was studied using allografts in a rat sciatic nerve model. Thirty male white Wistar rats were divided into 3 experimental groups (n = 10) randomly: normal control group (NC), allograft group (ALLO), and IGF I-treated group (ALLO/IGF). In the NC group, the left sciatic nerve was exposed through a gluteal muscle incision and, after homeostasis, the muscle was sutured. In the ALLO group, the left sciatic nerve was exposed through a gluteal muscle incision and transected proximal to the tibioperoneal bifurcation where a 10-mm segment was excised. The same procedure was performed in the ALLO/IGF group. The harvested nerves of the rats of the ALLO group were served as allograft for the ALLO/IGF group and vice versa. The NC and ALLO groups received 10 μL of sterile phosphate buffered saline intraperitoneally once a day for 1 week, and the ALLO/IGF group received 10 μL of IGF I (100 ng/kg per day) intraperitoneally once a day for 1 week. Behavioral testing, sciatic nerve functional study and the gastrocnemius muscle mass showed earlier regeneration of axons in the ALLO/IGF group than in the ALLO group (P < 0.05). Administration of IGF I could accelerate functional recovery after nerve allografting in the sciatic nerve and may have clinical implications for the surgical management of patients after facial nerve transection.
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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) | 0.000 | 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".