Agreement between Histomorphometry and Microcomputed Tomography to Assess Bone Microarchitecture of Dental Implant Sites
Bibliographic record
Abstract
BACKGROUND: Histomorphometry and microcomputed tomography (microCT) have been used in implant studies but need better understanding before being used as equivalent methods. PURPOSE: The purpose of this study was to investigate the agreement between 2D (histomorphometry) and 3D (microCT) reference methods for assessing jawbone microarchitecture in vivo. MATERIAL AND METHODS: Forty-four bone specimens from 32 patients were obtained during implant placement and examined by microCT, followed by hematoxylin-eosin staining and histomorphometric analysis. The morphometric parameters included bone volume density (BV/TV), bone surface fraction (BS/TV), bone surface density, trabecular thickness, trabecular number, and trabecular separation (Tb.Sp). Bland-Altman plots were used for pairwise agreement analysis between the equivalent 3D and 2D parameters, and complemented with Mountain plots. The association between the two methods was tested using Pearson's correlation followed by Passing-Bablok regression. RESULTS: Systematic bias was observed in all Bland-Altman and Mountain plots, including constant bias for BV/TV and Tb.Sp, and proportional bias for all other parameters. Significant correlation was found for BV/TV (r = 0.80; p < .001) and BS/TV (r = 0.44; p = .003), and the Passing-Bablok regression showed constant bias for BV/TV and proportional bias for BS/TV. CONCLUSION: Because of the poor agreement between measures obtained by histomorphometry and microCT, these methods should not be used interchangeably for jawbones.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".