Inconsistencies in the Differential Diagnosis of Open Bite
Bibliographic record
Abstract
OBJECTIVE: To examine inconsistencies in the differential diagnosis of open bite. MATERIALS AND METHODS: Using visual judgments, a total of 77 anterior open-bite cases in the postpubertal growth period were grouped as either morphogenetic, functional, or combination. The same sample was also grouped as either hyperdivergent, normodivergent, or hypodivergent using mandibular-plane angle and conventional cephalometry. Kappa analysis was used to test the agreement between the 2 methods of evaluation, and chi2 tests were used to analyze the distribution of cephalometrically grouped hyperdivergent, normodivergent, and hypodivergent cases among the visually assessed morphogenetic, functional and combination groups and vice versa. A kappa score of 0.343 indicated a weak agreement between visual judgment and cephalometric methods of evaluation (P < .001). RESULTS: Despite the expectation that cases evaluated as hyperdivergent using cephalometry would be visually evaluated as morphogenetic, more than half of the cases assessed as hyperdivergent were in fact classified as functional. CONCLUSIONS: These findings highlight the inadequacy of relying solely on cephalometric evaluation to classify open bite.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".