Correlation between Facial Soft Tissues and Vertical Facial Pattern in 12-16 Years Old Untreated Patients
Bibliographic record
Abstract
Background: Soft tissue paradigm shift has accentuated significance of soft tissue variables in diagnosis & treatment planning. Aim: To find a correlation between facial soft tissues and underlying vertical facial patterns in young untreated patients. Methods: The lateral cephalograms of 170 young individuals were divided into three equal groups, i.e., long, average, and short face, in accordance with the vertical facial patterns. This was done using a cross-sectional research design. Upper and lower lip lengths and extent of lip protrusion were measured for each individual. Non-probability consecutive sampling was done. The relationship between face soft tissue and the vertical facial pattern was examined using the Pearson Correlation test and less than 0.05 p-value was held statistically significant. Result: Significant correlation between upper and lower lip lengths and vertical facial form was found. Similarly significant positive correlation between protrusion of upper and lower lips and vertical facial pattern was found. Conclusion: Cephalometric analyses suggest the vertical dimensions of facial soft tissues conform to the vertical skeletal patterns. The long facial patterns have increased lip lengths and procumbent lips. MeSH words: Cephalometric analysis, Cross-sectional study, Vertical facial pattern, Lip length
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".