The impact of Cone Beam CT on financial costs and orthodontists’ treatment decisions in the management of maxillary canines with eruption disturbance
Bibliographic record
Abstract
Background: Examination with Cone Beam CT (CBCT) is common for localizing maxillary canines with eruption disturbance. The benefits and costs of these examinations are unclear. Objectives: To measure: 1. the proportion of orthodontists' treatment decisions that were different based on intraoral and panoramic radiography (M1) compared with CBCT and panoramic radiography (M2); and 2. the costs of producing different treatment plans, regarding patients with maxillary canines with eruption disturbance. Subjects and methods: Orthodontists participated in a web-based survey and were randomly assigned to denote treatment decisions and the level of confidence in this decision for four patient cases presented with M1 or M2 at two occasions for the same patient case. Results: One hundred and twelve orthodontists made 445 assessments based on M1 and M2, respectively. Twenty-four per cent of the treatment decisions were different depending on which method the raters had access to, whereof one case differed significantly from all other cases. The mean total cost per examination was €99.84 using M1 and €134.37 using M2, resulting in an incremental cost per examination of €34.53 for M2. Limitations: Benefits in terms of number of different treatment decisions must be considered as an intermediate outcome for the effectiveness of a diagnostic method and should be interpreted with caution. Conclusions: For the patient cases presented in this study, most treatment decisions were the same irrespective of radiological method. Accordingly, this study does not support routine use of CBCT regarding patients with maxillary canine with eruption disturbance.
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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 | 0.012 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".