Accuracy of the electronic method versus the radiographic method in determining working length: integrative systematic review
Bibliographic record
Abstract
Introduction: The radiographic method is widely used to calculate the working length (CT). However, it presents limitations that, to overcome them, apical electronic locators (LEAs) were developed to allow greater accuracy and reliability in determining the CT. Objective: The present study aims to perform an integrative systematic review and gather the available scientific evidence on the method that allows greater accuracy in determining the CT between the radiographic method and the electronic localisation method (LEA). Materials and Methods: The literature search was performed on the PubMed platform using keywords combined through the Boolean operator AND as follows: ("endodontics" AND "electronic apex locator" AND "radiographic measurement") and ("endodontics" AND "electronic apex locator" AND "radiographic measurement" AND "odontometry"). Results: 68 articles reporting comparative studies performed between the methods, radiographic and LEA, were identified. Eleven studies were included, of which, in some cases, LEAs showed better results than non-digital periapical radiographs. The studies indicate that although there are differences between the two methods, with greater accuracy in determining LEA, the differences did not prove to be statistically significant. In the presence of irrigating solutions, sodium hypochlorite revealed more significant discrepancies in CT determination with the LEA method. Conclusion: The two methods, radiographic and LEA, should be used in association since each has particularities that present more significant benefits enhancing the success of endodontic treatment and a better and more complete treatment for patients.
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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 | 0.025 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".