<b>Defective bone repair in diclofenac treated C57Bl6 mice with and without lipopolysaccharide induced systemic inflammation</b>
Bibliographic record
Abstract
Bone repair after trauma or surgical intervention involves a tightly regulated cascade of events that starts with hemostasis and an inflammatory response, which are critical for successful healing. Nonsteroidal anti-inflammatory drugs (NSAID) are routinely prescribed for pain relief despite their potential inhibitory effect on bone repair. The goal of this study was to determine the impact of administration of the non-selective NSAID diclofenac in the inflammatory phase of bone repair in mice with or without lipopolysaccharide-induced systemic inflammation. Repair of femoral window defects was characterized using micro computed tomography imaging and histological analyses at 2 weeks postoperative. The data indicate (a) impaired bone regeneration associated with reduced osteoblast, osteoclast, and macrophage activity; (b) changes in the number, activity, and distribution of mast cells in regenerating bone; and (c) impaired angiogenesis due to a direct toxic effect of diclofenac on vascular endothelial cells. The results of this study provide strong evidence to support the conjecture that administration of NSAIDs in the first 2 weeks after orthopaedic surgery disrupts the healing cascade and exacerbates the negative effects of systemic inflammation on the repair process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".