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 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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".