Interobserver Reliability of Cervical Vertebral Maturation Staging
Bibliographic record
Abstract
Background: the accurate identification of a patient's maturation stage, particularly during the pubertal growth spurt, takes on heightened importance, particularly when addressing cases of dentoskeletal problem Active growth determination is crucial for orthodontic treatment. Objectives: The objective of this study was to determine interobserver reliability of Cervical Vertebrae Maturation (CVM) stagingMaterial and Methods: This cross-sectional correlational study, conducted in the Department of Orthodontics on 70 subjects. Ten orthodontic clinicians, including 4 orthodontists and 6 orthodontic residents, with no prior experience in CVM staging, underwent comprehensive training. The study involved assessing 70 lateral cephalograms with clear visibility of cervical vertebrae, evaluated by two independent examiners using the Baccetti et al. staging system. The collected data were analyzed using R programming, including descriptive statistics for age, Spearman's correlation, and Cohen's Kappa for assessing interobserver agreement, with stratification by gender. Results: The mean age of participants was 12.60±3.93 years, with 38 females (54.29%) and 32 males (45.71%). The study revealed varying levels of agreement between two observers for different CVM stages, ranging from perfect agreement in CS1 to strong agreement in CS3. CS4, CS5, and CS6 also showed varying agreement rates. Additionally, there was robust and statistically significant agreement (p<0.001) between observers in cervical maturation staging, supported by a high Spearman's rho value (r=0.927). Furthermore, substantial agreement was observed with a Kappa value of 0.748, indicating a strong consensus in cervical staging assessments.Conclusion: Cervical vertebral maturation staging is reliable in term of inter-observers agreement. Keywords: Cervical vertebral maturation, Interobserver agreement, Growth assessment
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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 | 0.019 | 0.038 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".