Root canal debridement using manual dynamic agitation or the EndoVac for final irrigation in a closed system and an open system
Bibliographic record
Abstract
Parente JM, Loushine RJ, Susin L, Gu L, Looney SW, Weller RN, Pashley DH, Tay FR. Root canal debridement using manual dynamic agitation or the EndoVac for final irrigation in a closed system and an open system. International Endodontic Journal, 43, 1001–1012, 2010. Abstract Aim This study examined canal debridement efficacy by testing the null hypothesis that there is no difference between a ‘Closed’ and an ‘Open’ system design in smear layer and debris removal using either manual dynamic agitation or the EndoVac for irrigant delivery. Methodology Forty teeth were divided into four groups and submitted to a standardized instrumentation protocol. Final irrigation was performed with either manual dynamic agitation or the EndoVac on groups of teeth with or without a sealed apical foramen. Smear and debris scores were evaluated using SEM and analysed using Cochran–Mantel–Haenszel statistic. Results The ability of manual dynamic agitation to remove smear layer and debris in a closed canal system was significantly less effective than in an open canal system and significantly less effective than the EndoVac (P < 0.001). Conclusion The null hypothesis was rejected; the presence of a sealed apical foramen adversely affected debridement efficacy when using manual dynamic agitation but not the EndoVac. Apical negative pressure irrigation is an effective method to overcome the fluid dynamics challenges inherent in closed canal systems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".