A 2-plane micro-computed tomographic alveolar bone measurement approach in mice
Bibliographic record
Abstract
PURPOSE: This study introduces a standardized 2-plane approach using 8 landmarks to assess alveolar bone levels in mice using micro-computed tomography. MATERIALS AND METHODS: (Pg) to controls. Eight measurements were obtained per tooth: 2 in the sagittal plane (mesial and distal) and 6 in the coronal plane (mesiobuccal, middle-buccal, distobuccal, mesiolingual, middle-lingual, and distolingual). RESULTS: Significant differences in the CEJ-to-ABC distance between Pg-infected mice and controls were found in the coronal plane (middle-lingual, mesiobuccal, and distolingual for the first molar; and mesiobuccal, middle-buccal, and distolingual for the second molar). In the sagittal plane, the distal measurement of the second molar was different. The middle-buccal, mesiobuccal, and distolingual sites of the first and second molars showed vertical bone loss relative to controls; the second molar middle-lingual site was also different. In the sagittal plane, the mesial sites of the first and second molars and the distal site of the second molar showed loss. Significantly different vertical bone height percentages were found for the mesial and distal sites of the second molar (sagittal plane) and the middle-lingual and distolingual sites of the first molar(coronal plane). CONCLUSION: A reliable, standardized technique for linear periodontal assessments in mice is described. Alveolar bone loss occurred mostly on the lingual surface of the coronal plane, which is often omitted in studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".