ArticleImaging science in dentistry2017
Repeat analysis of intraoral digital imaging performed by undergraduate students using a complementary metal oxide semiconductor sensor: An institutional case study.
Article in Imaging science in dentistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Effect of Centring Rings on Number of Retakes and Cone Cuts in Intraoral Imaging Performed by Dental Students: A Randomized Case-Control Study.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Trial
- Effect of Practical Demonstration on Reducing Technical Radiographic Errors Made by Dental Students.Journal of dental education · 2026Article
- Assessment of radiographic errors and repetition rates in undergraduate endodontic education: a retrospective clinical study.The Saudi dental journal · 2025Article
- Effect of quality of radiographs taken during root canal treatment on technical quality of root canal fillings and endodontic outcome.Restorative dentistry & endodontics · 2025Article
- Image Repeat Analysis in Conventional Radiography in Mobile Clinics: A Retrospective Observational Study.Acta medica Philippina · 2025Article
- Reject Rates of Radiographic Images in Dentomaxillofacial Radiology: A Literature Review.International journal of environmental research and public health · 2021Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study was performed to quantify the repeat rate of imaging acquisitions based on different clinical examinations, and to assess the prevalence of error types in intraoral bitewing and periapical imaging using a digital complementary metal-oxide-semiconductor (CMOS) intraoral sensor. MATERIALS AND
methodsA total of 8,030 intraoral images were retrospectively collected from 3 groups of undergraduate clinical dental students. The type of examination, stage of the procedure, and reasons for repetition were analysed and recorded. The repeat rate was calculated as the total number of repeated images divided by the total number of examinations. The weighted Cohen's kappa for inter- and intra-observer agreement was used after calibration and prior to image analysis.
resultsThe overall repeat rate on intraoral periapical images was 34.4%. A total of 1,978 repeated periapical images were from endodontic assessment, which included working length estimation (WLE), trial gutta-percha (tGP), obturation, and removal of gutta-percha (rGP). In the endodontic imaging, the highest repeat rate was from WLE (51.9%) followed by tGP (48.5%), obturation (42.2%), and rGP (35.6%). In bitewing images, the repeat rate was 15.1% and poor angulation was identified as the most common cause of error. A substantial level of intra- and interobserver agreement was achieved.
conclusionThe repeat rates in this study were relatively high, especially for certain clinical procedures, warranting training in optimization techniques and radiation protection. Repeat analysis should be performed from time to time to enhance quality assurance and hence deliver high-quality health services to patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.