Fs‐laser ablation of teeth is temperature limited and provides information about the ablated components
Bibliographic record
Abstract
Abstract The goal of this work is to investigate the thermal effects of femtosecond laser (fs‐laser) ablation for the removal of carious dental tissue. Additional studies identify different tooth tissues through femtosecond laser induced breakdown spectroscopy (fsLIBS) for the development of a feedback loop that could be utilized during ablation in a clinical setting. Scanning Election Microscope (SEM) images reveal that minimal morphological damages are incurred at repetition rates below the carbonization threshold of each tooth tissue. Thermal studies measure the temperature distribution and temperature decay during laser ablation and after laser cessation, and demonstrate that repetition rates at or below 10kHz with a laser fluence of 40 J/cm2 would inflict minimal thermal damage on the surrounding nerve tissues and provide acceptable clinical removal rates. Spectral analysis of the different tooth tissues is also conducted and differences between the visible wavelength fsLIBS spectra are evident, though more robust classification studies are needed for clinical translation. These results have initiated a set of precautionary recommendations that would enable the clinician to utilize femtosecond laser ablation for the removal of carious lesions while ensuring that the solidity and utility of the tooth remain intact.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".