A Microcomputed Tomographic Evaluation of Root Canal Morphology of Maxillary Second Premolars in a Pakistani Cohort
Bibliographic record
Abstract
This study aimed to investigate variations in the root canal morphology of maxillary second premolar (MSP) teeth using microcomputed tomography (micro-CT). Sixty (N = 60) human extracted MSPs were collected and prepared for micro-CT scanning. The duration for scanning a single sample ranged between 30 and 40 min and a three-dimensional (3-D) image was obtained for all the MSPs. The images were evaluated by a single observer who recorded the canal morphology type, number of roots, canal orifices, apical foramina(s), apical delta(s), and accessory canals. The root canal configuration was categorized in agreement with Vertucci’s classification, and any configuration not in agreement with Vertucci’s classification was reported as an “additional canal configuration”. Descriptive statistics (such as mean percentages) were calculated using SPSS software. The most common types agreeing with Vertucci’s classification (in order of highest to lowest incidence) were types I, III, V, VII, II, and VI. The teeth also exhibited four additional configurations that were different from Vertucci’s classification: types 2-3, 1-2-3, 2-1-2-1, and 1-2-1-3. A single root was found in 96.7% and the majority of the samples demonstrated two canals (73.3%). Further, 80% of the teeth showed one canal orifice. The number of apical foramina’s in the teeth was variable, with 56.7% having solitary apical foramen. The accessory canal was found in 33.3%, and apical delta was found in only 20% of the samples. Variable morphology of the MSPs was detected in our study. The canal configuration most prevalent was type 1; however, the results also revealed some additional canal types.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".