Analysis of the instrumentation time and cleaning between manual and rotary techniques in deciduous molars
Bibliographic record
Abstract
The rotary instrumentation provides shorter instrumentation time with greater comfort for the patient but few studies have been conducted on primary teeth. Objective: this study compared the cleaning ability and instrumentation time between manual and rotary techniques in deciduous molars. Material and methods: a total of 15 molars were selected, submitted to coronal opening and root canal filled with India ink. After 48 hours, the teeth were divided into three groups: G1 – manual instrumentation with K files, G2 – rotary system Endowave, and G3 – rotary system ProTaper. After instrumentation, the teeth were sectioned and three blinded examiners evaluated the root canal cleaning. The mode of scores of examiners was analyzed by the Kruskal-Wallis test. The instrumentation time was recorded and the results were statistically analyzed by ANOVA. Results: the ProTaper system presented shorter instrumentation time compared to manual instrumentation (p = 0.0339). Endowave system did not present statistically significant difference in the instrumentation time compared to the other groups. There were no significant differences between groups concerning the ability of root canal cleaning (p = 0.6188). Conclusion: ProTaper system revealed shorter treatment time and similar cleaning ability compared to the other techniques, thus being indicated for deciduous teeth.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".