The critical‐size supraalveolar peri‐implant defect model: reproducibility in histometric data acquisition of alveolar bone formation and osseointegration
Bibliographic record
Abstract
OBJECTIVE: The objective of this report is to present the reproducibility of outcomes assessments in the Critical-Size Supraalveolar Peri-Implant Defect Model. MATERIALS AND METHODS: Two examiners without specific experience in histological analysis and one experienced examiner performed the histometric evaluation. A comprehensive training program in data acquisition and histological analysis was established, the inexperienced examiners underwent approximately 12 h of training over multiple sessions. A custom-designed image analysis software macro and a computer-based image system were used to analyse digital images generated by a microscope camera system. Nine parameters for newly formed and resident bone were evaluated. Examiners performed histometric analysis using 36 histologic sections selected from critical-size supraalveolar peri-implant defects in 12 male Hound Labrador Mongrel dogs. Buccal and lingual measurements were performed in 72 sites. Intra- and inter-examiner reproducibility were evaluated using the concordance correlation coefficient (CCC) and means +/- SD of the differences. Systematic errors were evaluated using an F-test for equality of means and variances. RESULTS: Intra-examiner reproducibility was high for all parameters evaluated, the lowest CCC observed being 0.87. Inter-examiner reproducibility was also high, most CCCs exceeding 0.90. Minor systematic errors for intra- and inter-examiner comparisons were occasionally observed. The results imply a high temporal stability because recordings were performed 3 months apart. Measurement errors were stable throughout the range of observations for all parameters. CONCLUSIONS: High examiner reproducibility and temporal stability can be achieved for histometric data acquisition using the Critical-Size Supraalveolar Peri-Implant Defect Model. Examiner reproducibility should be routinely assessed, reported, and accounted for to assure the quality of evidence generated by preclinical studies.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".