Effectiveness of Six Irrigation Techniques With Sodium Hypochlorite in Tissue Dissolution
Bibliographic record
Abstract
Introduction: Complete removal of pulp tissue by mechanical instrumentation remains a challenge. The study aimed to assess the efficacy of six irrigation techniques using 3% sodium hypochlorite (NaOCl) in dissolving tissue. Materials and methods: Seventy standardized fragments of bovine muscle tissue were divided into seven groups and exposed to different irrigation devices, with a maximum of 10 minutes of irrigation at room temperature. The devices included erbium-doped yttrium-aluminum-garnet (Er:YAG) laser (LightWalker® ST-E; Fotona d.o.o., Ljubljana, Slovenia), erbium, chromium-doped yttrium, scandium, gallium and garnet (Er,Cr:YSGG) laser A (EdgePRO™; Biolase Technology Inc., San Clemente, California, United States), Er,Cr:YSGG laser B (WaterLase iPlus®; Biolase, California, United States), multisonic ultracleaning system (MUS) GentleWave® system with CleanFlow™ handpiece (Sonendo, Inc., Laguna Hills, California, United States), passive ultrasonic irrigation (PUI), EndoUltra ultrasonic activator (Vista Apex Dental Products, Racine, Wisconsin, United States), and conventional needle irrigation (CNI), a 29G NaviTip irrigation needle at a flow rate of 5 mL/min (Ultradent Products, Inc., Utah, United States), with 3% NaOCl without agitation as the control. For samples that dissolved completely within 10 minutes, the dissolution time was recorded, and for other samples, the tissue weight was measured after the 10-minute irrigation experiment. The tissue dissolution rate (%/s) was calculated for all experiments. Results: MUS had the fastest tissue dissolution rate (P < 0.01), at 2.986% ± 0.395% per second. The laser systems had dissolution rates of 0.388% ± 0.062% per second for Er:YAG laser, 0.316% ± 0.042% for Er,Cr:YSGG laser A, and 0.258% ± 0.018% for Er,Cr:YSGG laser B. CNI and PUI showed the slowest tissue dissolution rates. Conclusions: MUS with CleanFlow, followed by Er:YAG and Er,Cr:YSGG lasers, achieved a significantly faster tissue dissolution rate than conventional irrigation methods of PUI and CNI tested. MUS with CleanFlow can greatly improve the effectiveness of NaOCl in tissue dissolution.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".