The Influence of a New Clinical Motion for Endodontic Instruments on the Incidence of Postoperative Pain.
Bibliographic record
Abstract
OBJECTIVES: Previous studies showed that motor motions play an important role in determining apical extrusion of debris. Therefore a new clinical motion (MIMERACI) has been proposed. The basic idea is to progress slowly (1mm advancement), and after each 1mm, to remove the instrument from the canal, clean flutes and irrigate. The aim of the study was to prove whether the clinical use of MIMERACI technique would influence or not postoperative pain. MATERIALS AND METHODS: 100 teeth requesting endodontic treatment were selected for the study and divided into two similar groups based on anatomy, pre-operative symptoms and vitality, presence or absence of periapical lesion. All teeth were shaped, cleaned and obturated by the same operator, using the same NiTi instruments. The only difference between the two groups was the instrumentation technique: tradional (group A) vs MIMERACI (group B). Assessment of postoperative pain was performed 3 days after treatment. Presence, absence and degree of pain were recorded with a visual analogue scale (VAS), validated in previous studies. Collected data statistically analyzed using one-way ANOVA post hoc Tukey test. RESULTS: For VAS pain scores MIMERACI technique showed significantly better results than group A (p=0,031). Overall, both incidence and intensity of symptoms were significantly lower. Flare ups occurred in 3 patients, but none treated with the MIMERACI Technique. CONCLUSIONS: Since extruded debris can elicit more postoperative pain, results obtained by using MIMERACI technique are probably due to many factors: better mechanical removal and less production of debris and more efficient irrigation during instrumentation.
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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 | 0.004 |
| 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) | 0.002 | 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".