Bibliographic record
Abstract
Introduction: It is important for a dental practitioner to have a clear understandingof the root canal morphology and its variations to perform successful root canal treatment.The inability to identify and adequately treat all canals of root canal system may contribute tothe failure of root canal treatment. Objectives: Clinically determine the frequency or numbersof root canals per tooth in the maxillary second molar teeth in the local population. Setting:Department of Operative Dentistry in Punjab Dental Hospital / de`Montmorency College ofDentistry, Lahore. Study Design: Randomized Control Trial. Study Period: 25th May 2013 to24th November 2013 (6 months). Results: This was a Cross sectional survey of 80 patients withsymptomatic irreversible pulpitis in maxillary second molar teeth in patients undergoing rootcanal treatment. The results showed that five (6.25%) patients had single root canal, seventeen(21.25%) patients had 2 root canals, forty (50%) patients had 3 root canals, seventeen (21.25%)patients had 4 root canals and one (1.25 %) patient had 5 root canals per tooth. In patientwith five canals, single root canal was present in distobuccal and palatal root each while threeroot canals were present in mesiobuccal root as MB-1, MB-2 and MB-3 canal. Conclusion:Local population have a lot of variations in root canal anatomy in second molar. So preclinicalknowledge can increase the success rate of root canal treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.019 |
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".