Effect of Canal Anastomosis on Periapical Fluid Pressure Build-up during Needle Irrigation in Single Roots with Double Canals using a Polycarbonate Model
Bibliographic record
Abstract
Sodium hypochlorite is an effective irrigant for chemical debridement of root canals. However, increasing the intracanal pressure during irrigant delivery may result in irrigant extrusion into the bone and soft tissues surrounding the tooth. Because clinicians often encounter teeth with intracanal communications, the objective of the present study was to examine the effects of canal anastomosis on the generation of periapical fluid pressure at different fluid flow rates and insertion depths. Two similar polycarbonate models were used to simulate a single root with double canals, one containing, and the other without communicating channels between the canals. For both models, periapical pressure increased with increasing irrigant flow rates and insertion depths of a 30-gauge side-venting needle. In the presence of communicating channels, the magnitude of pressure build-up decreased by almost 90% irrespective of the fluid flow rate or needle insertion depth. Pressure reduction in anastomoses-containing roots provides an explanation why pressure generation in single roots is considerably higher. Nevertheless, it is still possible in teeth with canal anastomoses for pressure exceeding the intraosseous pressure to be generated when the fluid flow rate is sufficiently high and when the needle tip is close to the apical terminus.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".