Non-steroidal anti-inflammatory drugs and bone healing in animal models—a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAID) have excellent anti-inflammatory and analgesic properties and are extensively used to treat post-traumatic or surgical musculoskeletal pain. Although an extensive literature exists on the administration of NSAID on animal bone healing, no systematic review and meta-analysis of animal studies that investigate the effect of NSAID administration on bone fracture healing. Objective of this study is to conduct a systematic review and meta-analysis to estimate the effect of NSAIDs administration on bone healing biomechanical and histomorphometric measurements in different animal models after bone fracture surgery. METHODS: We performed a systematic review and meta-analysis of animal studies to estimate the effect of NSAID administration after bone fracture on healing outcomes. We searched eight databases without limiting the search to starting date up to 1 February 2021 for articles on fractured bone healing in animal models in which NSAID were administered. RESULTS: Out of 6732 articles screened, 47 were included and 3 common bone healing outcomes were analysed: biomechanical properties (maximum force to break, stiffness, and work-to-failure), micro-computed tomography (μ-CT), and histomorphometric measurements. The studies were generally of low-quality scores because crucial information, especially concerning randomization, blinding, and allocation concealment, was poorly reported. Our results show that the negative effects of NSAID after bone fracture on certain biomechanical properties of the healing bones was not statistically significant in mice compared with other animals, in females compared with males, and in younger compared with older animals. CONCLUSION: The findings demonstrated that NSAIDs administration decreased the biomechanical properties of healing bones after fracture surgery in comparison to the control group. Moreover, different effect on certain outcomes was detected among different sites, sex of the animals, and the time of assessment. TRIAL REGISTRATION: Protocol published and registered in SYstematic Review Center for Laboratory animal Experimentation (SYRCLE) in 2017, https://www.radboudumc.nl/getmedia/757ec408-7a9e-4635-8233-ae951effea54/Non-Steroidal-Anti-inflammatory-Drugs-and-bone-healing-in-animal-Models-Systematic-Review-and-Meta-Analysis.aspx.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".