Changes in centring and shaping ability using three nickel–titanium instrumentation techniques analysed by micro‐computed tomography (μCT)
Bibliographic record
Abstract
AIM: To compare the centring ability and the shaping ability of ProTaper (PT) files used in reciprocating motion and PT and Twisted Files (TF) used in continuous rotary motion, and to compare the volume changes obtained with the different instrumentation techniques using micro-computed tomography. Methodology Sixty mesial canals of thirty mandibular molars were randomly assigned to three instrumentation techniques: group 1, canals prepared with the PT series (up to F2) (n = 20); group 2, canals prepared with the F2 PT in reciprocating motion (n = 20); group 3 canals prepared with the TF series (size 25) (n = 20). Teeth were scanned pre- and postoperatively using micro-computed tomography to measure volume and shaping changes, and the obtained results were statistically analysed using parametric tests. Results The increase in canal volume obtained with the three instrumentation techniques was not significantly different. Canals were transported mostly towards the mesial aspect in the apical- and mid-third of the roots, and towards the furcal aspect coronally. No difference in the transportation and centring ratio was found between the techniques. There was no significant difference between the times of instrumentation (TF: 62.5 ± 5.4 s; PT: 60.6 ± 3.9 s; and F2 PT file in reciprocating motion: 51.0 ± 3.3 s). Conclusions ProTaper files used in reciprocating motion and PT and TF used in continuous rotary motion were capable of producing centred preparations with no substantial procedural errors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".