Diagnosis of ankylosis in permanent incisors by expert ratings, Periotest<sup>®</sup> and digital sound wave analysis
Bibliographic record
Abstract
The objectives of this investigation were to: (i) assess the reliability of expert raters to detect ankylosis from recordings of percussion sounds, (ii) measure differences in Periotest values (PTV) between ankylosed and non-ankylosed incisors and (iii) identify characteristic differences in recorded percussion sounds from ankylosed and non-ankylosed incisors using digital sound wave analysis. A convenience sample of healthy children (age range 7-18 years) was invited to participate. Ankylosis group children had one or more documented ankylosed maxillary incisors. Control group children had intact, non-ankylosed incisors. Digital recordings of percussion sounds and PTV were acquired for each incisor of interest. Four experienced pediatric dentists rated the randomized percussion sound pairs for the presence of ankylosis. Percussion sounds were also subjected to digital sound wave analysis. Overall agreement for the expert raters was substantial (kappa = 0.7). Intra-rater agreement was substantial to almost perfect (kappa = 0.6-0.9). Diagnosis of ankylosis demonstrated sensitivity of 76-92% and specificity of 74-100%. PTV from ankylosed incisors were statistically lower than PTV from non-ankylosed incisors. Ankylosed incisor digital sound wave signals exhibited significantly more energy in high-frequency bands than non-ankylosed incisors. This investigation demonstrated that: (i) experienced pediatric dentists reliably detected ankylosis by percussion sound alone; (ii) PTV for ankylosed incisors were statistically lower than PTV from non-ankylosed incisors; and (iii) ankylosed incisors exhibited a higher proportion of their signal energy in high-frequency bands.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".