Identification of Possible Factors Impacting Dental Students’ Ability to Locate MB2 Canals in Maxillary Molars
Bibliographic record
Abstract
This study examined the effect of the access size and straight-line path of access on third-year dental students' ability to locate a second mesiobuccal (MB2) canal in maxillary first and second molars. One hundred and six third-year dental students at one Faculty of Dentistry performed simulated root canal treatment with the aid of 2x magnification loupes on extracted teeth. A postgraduate endodontic student subsequently made a reasonable search for an untreated MB2 canal with the aid of a dental operating microscope. The mesiobuccal roots were then sectioned horizontally for determination of the canal configuration. The dental students were able to treat an MB2 canal in 15.8 percent of the teeth, but this was not associated with satisfactory access criteria. The postgraduate endodontic student identified an MB2 canal in 54.7 percent of the remaining tooth samples excluding those where the MB2 canal was found by the dental students; this represented 94.3 percent of those teeth confirmed by horizontal sectioning of the root to have an MB2 canal. The postgraduate student troughed, on average, 2.6 mm before negotiating the MB2 canal. As satisfactory access criteria and straight-line path of access did not correlate with the dental students' ability to find a second mesiobuccal canal, this result has important implications for educational goals with respect to endodontic treatment of maxillary molar 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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".