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- MakaleAçık Erişim2026Scopus SJRQ3Fracture resistance of roots obturated with bioceramic or resin-based sealers after nanohydroxyapatite and calcium hydroxide dressing
Özdemir, Olcay; Kaşıkçı, Sena; Şirinoğlu, Ebru; Kopac, Turkan
This study evaluated the effect of calcium-rich nanohydroxyapatite (nano-HA) as an intracanal medicament on root fracture resistance compared with calcium hydroxide (CH). Forty-eight caries-free maxillary central incisors were instrumented and randomly assigned to a two-factor design (two medicaments × two sealers), yielding four experimental groups (n = 12/group): CH + resin-based sealer, CH + bioceramic-based sealer, nano-HA + resin-based sealer, and nano-HA + bioceramic-based sealer. The medicaments were applied and stored in simulated body fluid at 37 °C and 100% humidity for 10 days. After removal, the canals were obturated with heated gutta-percha using either a resin-based or a bioceramic-based sealer, and were then stored for an additional 28 days. Vertical fracture resistance was measured using an Instron Universal Testing Machine. Data were analyzed using two-way ANOVA (p < 0.05). The nano-HA groups showed a higher mean fracture resistance (924.04 N) than the CH groups (818.54 N). The highest fracture resistance was observed in the nano-HA + bioceramic-based sealer group (1020.38 N), whereas the lowest was observed in the CH + resin-based sealer group (665.83 N). The main effect of sealer type was statistically significant (p < 0.001), while no significant interaction between medicament and sealer type was detected. Within the limitations of this in vitro study using static vertical loading, sealer type had a large, statistically significant effect on fracture resistance, with the bioceramic-based sealer yielding higher values than the resin-based sealer. Although the nano-HA groups exhibited numerically higher fracture resistance than calcium hydroxide, the main effect of intracanal medicament did not reach statistical significance. These findings are limited to laboratory conditions and should not be directly extrapolated to long-term clinical performance. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
- MakaleYalnız künye2026Scopus SJRQ1Impact of guideline-based prompting on the large language model performance in dental trauma management clinical decision-making
Kaşıkçı, Sena; Şirinoğlu, Ebru; Özdemir, Olcay
This study evaluated the impact of guideline-based prompting on the performance of large language models (LLMs) in answering dental trauma–related questions. Sixteen multiple-choice questions (MCQs) and sixteen open-ended questions (OEQs) derived from the International Association of Dental Traumatology (IADT) guidelines were used. ChatGPT-4o, Gemini-2.5 Flash, and DeepSeek v3.2 were tested under two conditions: with and without guideline support. Questions were asked three times daily over three consecutive days using independent chat sessions. In the guideline-based condition, the dental trauma guideline was uploaded and models were instructed to answer according to the document. Responses were evaluated using predefined answer keys and a structured rubric. Statistical analyses were performed using the Shapiro–Wilk test, aligned rank transform (ART) analysis, and Bonferroni-adjusted multiple comparisons. Multiple-choice performance was consistently high across all models, with no significant effects of day, time, or AI model. Guideline-based prompting substantially improved performance and guideline concordance on open-ended questions. In the guideline-supported condition, DeepSeek achieved a median score of 32, while ChatGPT and Gemini achieved median scores of 30. Without guideline support, median scores decreased to 26, 20, and 23, respectively. All models achieved 100% accuracy on MCQs with guideline support, whereas accuracy ranged from 81.2% to 100% without guideline support. Significant differences between AI models were observed for OEQ scores (p < 0.001). Guideline-based prompting improved guideline concordance and overall performance, particularly for open-ended dental trauma questions. These findings support the use of guideline grounding to enhance the guideline concordance and reliability of LLM-generated responses while emphasizing the continued need for clinician oversight.
