The influence of different cheek and lip retractors and emissivity on intraoral infrared thermography
Bibliographic record
Abstract
OBJECTIVES: To assess the influence of two conventional and one adapted cheek and lip retractors and three emissivity setting values on intraoral infrared thermography (IT) temperature values. METHODS: The sample was composed by 50 volunteers. Three cheek and lip retractors were tested: Group 1-flex retractor (FR); Group 2-FR adapted with Styrofoam; Group 3-U-type retractor (UR) for cheek and lip. All thermograms were acquired using FLIR T650 infrared camera. A set of three thermograms in frontal norm were acquired for each lip and cheek retractor at 0.91, 0.96, and 0.98ε, with an interval of 15 min between each set of images to avoid thermal interference. All images were assessed by two observers. The ROIs' mean temperature of the four upper incisors was recorded. Two-way ANOVA and Sidak post-test were used for data assessment with a significance level of 5%. RESULTS: Group 3 showed higher mean temperature than Groups 1 and 2 at all emissivity settings for all assessed teeth (P < .05). 0.91ε showed higher temperature than 0.96ε and 0.98ε for all assessed variables (P < .01). Contralateral teeth assessed using Group 3 at 0.91ε showed statistical differences between each other (P < .05). No statistical difference was observed between contralateral teeth assessed using Groups 1 and 2 at 0.96ε and 0.98ε (P > .05). CONCLUSIONS: The choice of cheek and lip retractor and emissivity setting can interfere on intraoral IT temperature values. U-type cheek and lip retractor and 0.91ε setting should not be used for IT image acquisition when assessing dental tissues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".