Effectiveness of Photon-initiated Photoacoustic Streaming in Root Canal Models With Different Width or Taper
Bibliographic record
Abstract
Abstract Background: To evaluate the sterilization effect of photon-initiated photoacoustic streaming (PIPS) in different widths or different tapers root canal systems.Methods: Artificial root canal samples (n=480) were randomly divided into 3 groups (n=160/group). The canals were prepared into size #10/.02, #25/.02 or #25/.06. Four different irrigation solutions were activated with conventional needle irrigation (CNI) or laser-activated irrigation (LAI). Bacterial suspensions and biofilms were assessed with adenosine 5'-triphosphate (ATP) assay kit and fluorescent microscopy.Results: When the root canal taper is 0.02, size #10 with LAI had a significant reduction of the bacteria than #25 with CNI (P < .05). When the apical width is 25, taper 0.02 with LAI had a significant reduction of the bacteria than taper 0.06 with CNI (P < .05). 5.25% NaOCl attained superior antibacterial and bacteriostatic effects as compared to the other irrigants, followed by 2% NaOCl. 2% and 5.25% NaOCl combined with PIPS can effectively enhance the long-term antibacterial effect of root canal after incubation for 6 hours.Conclusions: Compared with CNI, PIPS have stronger ability to remove bacteria in root canals with a small preparation width and a small taper. PIPS with 2% and 5.25% NaOCl can have superior antibacterial and bacteriostatic effects.
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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 | 0.000 |
| 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 | 0.000 |
| 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".