Drill-Hole Bone Defects in Animal Models of Bone Healing: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Bone fractures are common conditions of the musculoskeletal system. Several animal models of bone fractures have been established to help elucidate the complex process of bone healing. In the last decades, drill-hole bone defects have emerged as a method to study bone healing. Animal models of drill-hole defects are easy to standardize and do not require external fixation of the bone. However, current studies of drill-hole bone defects lack detailed descriptions of techniques and interstudy standardization. OBJECTIVE: This systematic review aims to present a detailed description of the different methods used to induce drill-hole bone defects in long bones of laboratory animals and to provide a comprehensive overview of their methodology and potential for investigation of bone healing. METHODS: A systematic search of PubMed and Embase will be performed of abstracts containing variations of the following four keywords: "long bone," "drill-hole," "regeneration," and "animal model." Abstract screening and full-text screening will be performed independently by 2 reviewers, and data will be extracted to a predesigned extraction protocol. The primary outcome of the included studies is the technique used to create the drill-hole bone defect, and secondary outcomes are any measurements or analyses of bone defect and regeneration. A narrative synthesis will be used to present the primary outcome, while information on secondary outcomes will be displayed graphically. The study protocol follows the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-analysis Protocols) guidelines. RESULTS: Abstract and full-text screening is ongoing and is expected to be completed by October 2022. Data extraction will commence immediately after, and the manuscript is expected to be completed by December 2023. The systematic review will follow the PRISMA statement. CONCLUSIONS: The strength of this systematic review is that it provides a comprehensive methodological overview of the different drill-hole methods and their advantages and disadvantages. This will assist researchers in choosing which model to use when studying different aspects of bone healing. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews CRD42020213076; https://tinyurl.com/bp56wdwe. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34887.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".