How to determine the permeability for cement infiltration of osteoporotic cancellous bone
Bibliographic record
Abstract
Cement augmentation is an emerging surgical procedure in which bone cement is used to infiltrate and reinforce osteoporotic vertebrae. Although this infiltration procedure has been widely applied, it is performed empirically and little is known about the flow characteristics of cement during the injection process. We present a theoretical and experimental approach to investigate the intertrabecular bone permeability during the infiltration procedure. The cement permeability was considered to be dependent on time, bone porosity, and cement viscosity in our analysis. In order to determine the time-dependent permeability, ten cancellous bone cores were harvested from osteoporotic vertebrae, infiltrated with acrylic cement at a constant flow rate, and the pressure drop across the cores during the infiltration was measured. The viscosity dependence of the permeability was determined based on published experimental data. The theoretical model for the permeability as a function of bone porosity and time was then fit to the testing data. Our findings suggest that the intertrabecular bone permeability depends strongly on time. For instance, the initial permeability (60.89 mm(4)/N(*)s) reduced to approximately 63% of its original value within 18 seconds. This study is the first to analyze cement flow through osteoporotic bone. The theoretical and experimental models provided in this paper are generic. Thus, they can be used to systematically study and optimize the infiltration process for clinical practice.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".