HANDBOOK OF PULMONARY REHABILITATION. Editors: Paraschiva A. Postolache (Romania), Darcy D. Marciniuk (Canada). Nova Science Publishers, Inc., New York, USA
Bibliographic record
Abstract
Lateral skull radiographs are used to perform cephalometric measurements and to evaluate the skeletal component of the orthodontic anomalies. It is difficult for the patients to imagine the expected outcome of their orthodontic treatments. so visual treatment objectives (VTO) can be a significant motivating factor. It can be used to illustrate the expected growth. the outcome of orthognathic surgery and the amount of teeth movements. This study was aimed to evaluate the accuracy of the visual treatment objectives through analysis of predicted and actual treatment outcomes. The second objective was the comparison of the initial cephalometric measurements with the final ones and the visualized treatment objectives prediction accuracy. Material and methods: Using the AudaxCeph cephalometric program and Roth-Jarabak analysis, lateral skull radiographs of 27 patients were analyzed. Visual treatment objectives were assessed by modification of the dental parameters as well as of the skeletal bases where indicated. Results: Lateral skull radiographs taken after the orthodontic treatment were used to evaluate the final cephalometric measurements. Statistical comparison of the dental and skeletal measurements of the final radiographs and the visual treatment objectives did not show statistically significant differences of the most important parameters. However, the following measurements were significantly different when compared with the visual treatment objectives Ar-Go-N angle (degrees), Go-Ar distance (mm) and S-Ar / Ar-Go ratio. Conclusions: Using the VTO, the expected orthodontic outcome can be well estimated, and it can be considered an effective tool for planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.030 |
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".