3D visualization and quantification of rat cortical bone porosity using a desktop micro‐CT system: a case study in the tibia
Bibliographic record
Abstract
Although micro-computed tomography (micro-CT) has become the gold standard for assessing the 3D structure of trabecular bone, its extension to cortical bone microstructure has been relatively limited. Desktop micro-CT has been employed to assess cortical bone porosity of humans, whereas that of smaller animals, such as mice and rats, has thus far only been imaged using synchrotron-based micro-CT. The goal of this study was to determine if it is possible to visualize and quantify rat cortical porosity using desktop micro-CT. Tibiae (n = 10) from 30-week-old female Sprague-Dawley rats were imaged with micro-CT (3 μm nominal resolution) and sequential ground sections were then prepared. Bland-Altman plots were constructed to compare per cent porosity and mean canal diameter from micro-CT (3D) versus histology (2D). The mean difference or bias (histology-micro-CT; ±95% confidence interval) for per cent porosity was found to be -0.15% (±2.57%), which was not significantly different from zero (P= 0.720). Canal diameter had a bias (±95% confidence interval) of -5.73 μm (±4.02 μm) which was found to be significantly different from zero (P < 0.001). The results indicated that cortical porosity in rat bone can indeed be visualized by desktop micro-CT. Quantitative assessment of per cent porosity provided unbiased results, whereas direct analysis of mean canal diameter was overestimated by micro-CT. Thus, although higher resolution, such as that available from synchrotron micro-CT, may ultimately be required for precise geometric measurements, desktop micro-CT--which is far more accessible--is capable of yielding comparable measures of porosity and holds great promise for assessment of the 3D arrangement of cortical porosity in the rat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".