Root Temperature Variation during Gutta-Percha Removal Using Stainless Steel and NiTi Instruments
Bibliographic record
Abstract
Objective: To assess the temperature variation of gutta-percha removal with stainless steel and two NiTi instruments using infrared thermography and thermocouples. Material and Methods: 45 single-rooted teeth were divided into three groups (n = 15) according to the following gutta-percha removal instruments: Largo Peeso (L), Protaper Retreatment (PR), and Reciproc (R). Thermal analysis was conducted using a FLIR T650sc infrared thermography camera and three thermocouples. For infrared thermography assessment, the infrared camera was programmed to acquire thermograms every 15 seconds before the gutta-percha removal started until temperature normalization. Root temperature was assessed in the thermograms using FLIR tools software v6.4 with the straight-line tool along the long axis of the tooth and in the cervical, middle, and apical thirds of each tooth. The temperature from the thermocouples was recorded and registered for each root third. Inferential statistical analysis Kruskal-Wallis and post hoc Tukey tests were used. Results: For the infrared thermography camera, the highest median temperature value was found 15 seconds after guttapercha removal for the L technique (20.3°C), which presented the highest temperatures at all studied times. For thermocouples, the highest temperature was found in the middle third during gutta-percha removal with L (20.7°C). PR and R presented similar patterns of root temperature. Conclusion: Stainless-steel L temperature reaches values above 10°C; however, the exposure time was too short to cause injuries to the periodontium.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".