Linear measurements using virtual study models
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review of the literature to assess the reliability and validity of linear measurements using virtual vs plaster study models. MATERIALS AND METHODS: A search strategy was developed for four online databases, and references were further hand searched for studies additional papers. Three researchers determined the eligibility of papers by applying specific selection criteria and ultimately selected 17 papers. Grouped by virtual model acquisition type and the number of landmarks used in a given measurement, the data were weighted by sample size and analyzed in terms of the reliability and validity of linear measurements. RESULTS: The intrarater reliability was high for two-landmark and >two-landmark linear measurements performed on laser-acquired models or cone-beam computed tomography (CBCT)-acquired models and were similar to measurements on plaster models. Validity was high for two-landmark and >two-landmark linear measurements comparing laser-acquired models or CBCT-acquired models to plaster study models, and the weighted mean differences were clinically insignificant. Agreement of measurements was excellent, with less variability than correlation. Acquisition type had no perceived influences on reliability and validity. More than two-landmark measures tended to have higher mean differences than two-landmark measures. CONCLUSIONS: Virtual study models are clinically acceptable compared with plaster study models with regard to intrarater reliability and validity of selected linear measurements.
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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 | 0.028 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".