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Autism-specific screening and developmental surveillance in primary care aid identification of autism. In this study, we assessed primary care clinicians’ (PCCs’) reported confidence in screening scores from the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) and in their own diagnostic impressions. Four PCCs provided data for 50 children aged 16–36 months for whom they had any developmental concern. PCCs’ diagnostic impressions were “Definitely Autism” for 15 children (30%), “Unsure—Needs Further Evaluation” for 25 children (50%) and “Definitely Not Autism” for 10 children (20%). They reported High Confidence on the screener score in 33 cases (66%). Of the 17 cases for whom PCCs reported having Low Confidence on the M-CHAT-R, 14 children (82.3%) had a Low Likelihood score, with no significant association between M-CHAT-R likelihood and PCC’s confidence in the screening score. PCCs’ diagnostic impressions were concordant with the M-CHAT-R autism likelihood in 42% of cases, with a significantly higher mean in confidence rating when compared to the non-concordant cases. Language development and social engagement were the most frequently endorsed concerns by PCCs, with significant associations between these concerns and M-CHAT-R likelihood. Our results suggest that, when developmental concerns exist, PCCs may place greater confidence in the M-CHAT-R when scores indicate a higher likelihood of autism, and that confidence in their own diagnostic impressions may be associated with concordance with the screener score.

This chapter presents one format for writing up reports and provides useful guidelines for the type of information that should be presented and effective ways of presenting that information. The format for psychological assessment reports is designed to be a logical outgrowth of the hypothesis testing model. The chapter discusses the major sections of the psychological assessment report, including client description, relevant background information, behavioral observations, mental status evaluation (MSE), overall interpretation of test findings, diagnostic impressions, and recommendations. The relevant background information section should include two overarching, major areas: the information from the biopsychological evaluation and the information from the psychosocial evaluation. The MSE is the only section of the entire report that is generally aimed toward other mental health professionals. In the end, the entire purpose of psychological assessment is usually to provide clear, direct, specific, and useful recommendations for improving an individual’s functioning and life.

Objectives New generation intraoral scanners are promoted to be suitable for digital scans of long-span edentulous spaces and completely edentulous arches; however, the evidence is lacking. The current study evaluated the accuracy of intraoral scanning (IOS) in partially and completely edentulous arch models and analyzed the influence of operator experience on accuracy. Materials and methods Four different resin models (completely and partially edentulous maxilla and mandible) were scanned, using a new generation IOS device ( n = 20 each). Ten scans of each model were performed by an IOS-experienced and an inexperienced operator. An industrial high-precision scanner was employed to obtain reference scans. IOS files of each model-operator combination, their respective reference scan files ( n = 10 each; total = 80), as well as the IOS files from each model generated by the same operator, were superimposed ( n = 45; total = 360) to calculate trueness and precision. An ANOVA for mixed models and post hoc t tests for mixed models were used to assess group-wise differences (α = 0.05). Results The median overall trueness and precision were 24.2 μm (IQR 20.7–27.4 μm) and 18.3 μm (IQR 14.4–22.1 μm), respectively. The scans of the inexperienced operator had significantly higher trueness in the edentulous mandibular model ( p = 0.0001) and higher precision in the edentulous maxillary model ( p = 0.0004). Conclusion The accuracy of IOS for partially and completely edentulous arches in in vitro settings was high. Experience with IOS had small influence on the accuracy of the scans. Clinical relevance IOS with the tested new generation intraoral scanner may be suitable for the fabrication of removable dentures regardless of clinician’s experience in IOS.

Background The literature has not yet validated the use of intraoral scanners (IOSs) for full-arch (FA) implant impression. Hence, the aim of this in vitro study was to assess and compare the trueness of 12 different IOSs in FA implant impression. Methods A stone-cast model of a totally edentulous maxilla with 6 implant analogues and scanbodies (SBs) was scanned with a desktop scanner (Freedom UHD®) to capture a reference model (RM), and with 12 IOSs (ITERO ELEMENTS 5D®; PRIMESCAN® and OMNICAM®; CS 3700® and CS 3600®; TRIOS3®; i-500®; EMERALD S® and EMERALD®; VIRTUO VIVO® and DWIO®; RUNEYES QUICKSCAN®). Ten scans were taken using each IOS, and each was compared to the RM, to evaluate trueness. A mesh/mesh method and a nurbs/nurbs method were used to evaluate the overall trueness of the scans; linear and cross distances between the SBs were used to evaluate the local trueness of the scans. The analysis was performed using reverse engineering software (Studio®, Geomagics; Magics®, Materialise). A statistical evaluation was performed. Results With the mesh/mesh method, the best results were obtained by CS 3700® (mean error 30.4 μm) followed by ITERO ELEMENTS 5D® (31.4 μm), i-500® (32.2 μm), TRIOS 3® (36.4 μm), CS 3600® (36.5 μm), PRIMESCAN® (38.4 μm), VIRTUO VIVO® (43.8 μm), RUNEYES® (44.4 μm), EMERALD S® (52.9 μm), EMERALD® (76.1 μm), OMNICAM® (79.6 μm) and DWIO® (98.4 μm). With the nurbs/nurbs method, the best results were obtained by ITERO ELEMENTS 5D® (mean error 16.1 μm), followed by PRIMESCAN® (19.3 μm), TRIOS 3® (20.2 μm), i-500® (20.8 μm), CS 3700® (21.9 μm), CS 3600® (24.4 μm), VIRTUO VIVO® (32.0 μm), RUNEYES® (33.9 μm), EMERALD S® (36.8 μm), OMNICAM® (47.0 μm), EMERALD® (51.9 μm) and DWIO® (69.9 μm). Statistically significant differences were found between the IOSs. Linear and cross distances between the SBs (local trueness analysis) confirmed the data that emerged from the overall trueness evaluation. Conclusions Different levels of trueness were found among the IOSs evaluated in this study. Further studies are needed to confirm these results.

Background Placing implants deep sub-gingivally may affect the accuracy of implant impression techniques and the fit of final restoration. Purpose The aim of this in-vitro study was to evaluate the effect of soft tissue thickness on accuracy of conventional and digital implant impression techniques. Methods Four parallel implant analogues (A, B, C, D) placed in each of two epoxy resin models representing edentulous mandible covered by flexible polyurethane material with two different thickness two mm and four mm. A total of sixty impressions performed, thirty impressions for each model divided into four groups ( n  = 15 per group) GI (C2mm) open tray impression with two mm implant depth, GII (C4mm) open tray impression with four mm implant depth, GIII (D2mm) digital impression with two mm implant depth, GIV (D 4 mm) digital impression with four mm implant depth. Impressions from open tray technique were poured to get stone casts while impressions from digital scanning technique were printed as three-dimensional printed casts. The six inter-implant distances between analogues were measured using Co-ordinate measuring machine, deviations compared to reference models were calculated. Data was collected, tabulated and statistically analyzed using One-way ANOVA test to detect significances between groups. Results For conventional impressions there was significant difference between C2mm/C4mm ( P  < 0.001) regarding interimplant distance, while in digital impressions there was no significant difference between D2mm/D4mm AB( p  = 0.110), BC( p  = 0.066), CD( p  = 0.710), AD( p  = 0.084), AC( p  = 0.067) and BD( p  = 0.072). There was significant difference between conventional and digital impression techniques C2mm/D2mm, C4mm/D4mm ( P  < 0.001). Conclusion Within the limitations of this in-vitro study digital impressions provide more accurate outcomes with implants placed deeper subgingivally than conventional impressions. Trial registration Retrospectively registered.

Objectives This study aimed to evaluate the accuracy of intraoral scanners in full-arch scans. Materials and methods A representative model with 14 prepared abutments was digitized using an industrial scanner (reference scanner) as well as four intraoral scanners (iTero, CEREC AC Bluecam, Lava C.O.S., and Zfx IntraScan). Datasets obtained from different scans were loaded into 3D evaluation software, superimposed, and compared for accuracy. One-way analysis of variance (ANOVA) was implemented to compute differences within groups (precision) as well as comparisons with the reference scan (trueness). A level of statistical significance of p  < 0.05 was set. Results Mean trueness values ranged from 38 to 332.9 μm. Data analysis yielded statistically significant differences between CEREC AC Bluecam and other scanners as well as between Zfx IntraScan and Lava C.O.S. Mean precision values ranged from 37.9 to 99.1 μm. Statistically significant differences were found between CEREC AC Bluecam and Lava C.O.S., CEREC AC Bluecam and iTero, Zfx Intra Scan and Lava C.O.S., and Zfx Intra Scan and iTero ( p  < 0.05). Conclusions Except for one intraoral scanner system, all tested systems showed a comparable level of accuracy for full-arch scans of prepared teeth. Further studies are needed to validate the accuracy of these scanners under clinical conditions. Clinical relevance Despite excellent accuracy in single-unit scans having been demonstrated, little is known about the accuracy of intraoral scanners in simultaneous scans of multiple abutments. Although most of the tested scanners showed comparable values, the results suggest that the inaccuracies of the obtained datasets may contribute to inaccuracies in the final restorations.

Backgrounds Intraoral scanner (IOS) accuracy is commonly evaluated using full-arch surface comparison, which fails to take into consideration the starting position of the scanning (scan origin). Previously a novel method was developed, which takes into account the scan origin and calculates the deviation of predefined identical points between references and test models. This method may reveal the error caused by stitching individual images during intraoral scan. This study aimed to validate the novel method by comparing the trueness of seven IOSs (Element 1, Element 2, Emerald, Omnicam, Planscan, Trios 3, CS 3600) to a physical impression digitized by laboratory scanner which lacks linear stitching problems. Methods Digital test models of a dentate human cadaver maxilla were made by IOSs and by laboratory scanner after polyvinylsiloxane impression. All scans started on the occlusal surface of the tooth #15 (universal notation, scan origin) and finished at tooth #2. The reference model and test models were superimposed at the scan origin in GOM Inspect software. Deviations were measured between identical points on three different axes, and the complex 3D deviation was calculated. The effect of scanners, tooth, and axis was statistically analyzed by the generalized linear mixed model. Results The deviation gradually increased as the distance from scan origin increased for the IOSs but not for the physical impression. The highest deviation occurred mostly at the apico-coronal axis for the IOSs. The mean deviation of the physical impression (53 ± 2 μm) was not significantly different from the Trios 3 (156 ± 8 μm) and CS 3600 (365 ± 29 μm), but it was significantly lower than the values of Element 1 (531 ± 26 μm), Element 2 (246 ± 11 μm), Emerald (317 ± 13 μm), Omnicam (174 ± 11 μm), Planscan (903 ± 49 μm). Conclusions The physical impression was superior compared to the IOSs on dentate full-arch of human cadaver. The novel method could reveal the stitching error of IOSs, which may partly be caused by the difficulties in depth measurement.

The aim of this study was to evaluate the trueness of 5 intraoral scanners (IOSs) for digital impression of simulated implant scan bodies in a partially edentulous model. A 3D printed partially edentulous mandible model made of Co-Cr with a total of 6 bilaterally positioned cylinders in the canine, second premolar, and second molar area served as the study model. Digital scans of the model were made with a reference scanner (steroSCAN neo) and 5 IOSs (CEREC Omnicam, CS3600, i500, iTero Element, and TRIOS 3) (n = 10). For each IOS's dataset, the XYZ coordinates of the cylinders were obtained from the reference point and the deviations from the reference scanner were calculated using a 3D reverse engineering program (Rapidform). The trueness values were analyzed by Kruskal-Wallis test and Mann-Whitney post hoc test. Direction and amount of deviation differed among cylinder position and among IOSs. Regardless of the IOS type, the cylinders positioned on the left second molar, nearest to the scanning start point, showed the smallest deviation. The deviation generally increased further away from scanning start point towards the right second molar. TRIOS 3 and i500 outperformed the other IOSs for partially edentulous digital impression. The accuracy of the CEREC Omnicam, CS3600, and iTero Element were similar on the left side, but they showed more deviations on the right side of the arch when compared to the other IOSs. The accuracy of IOS is still an area that needs to be improved.

Exploring the generative capabilities of the multimodal GPT-4, our study uncovered significant differences between radiological assessments and automatic evaluation metrics for chest x-ray impression generation and revealed radiological bias.

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various natural language processing tasks, particularly in text generation. However, their effectiveness in summarizing radiology report impressions remains uncertain. This study aims to evaluate the capability of nine LLMs, that is, Tongyi Qianwen, ERNIE Bot, ChatGPT, Bard, Claude, Baichuan, ChatGLM, HuatuoGPT, and ChatGLM-Med, in summarizing Chinese radiology report impressions for lung cancer. We collected 100 Chinese computed tomography (CT), positron emission tomography (PET)-CT, and ultrasound (US) reports each from Peking University Cancer Hospital and Institute. All these reports were from patients with suspected or confirmed lung cancer. Using these reports, we created zero-shot, one-shot, and three-shot prompts with or without complete example reports as inputs to generate impressions. We used both automatic quantitative evaluation metrics and five human evaluation metrics (completeness, correctness, conciseness, verisimilitude, and replaceability) to assess the generated impressions. Two thoracic surgeons (SZ and BL) and one radiologist (QL) compared the generated impressions with reference impressions, scoring them according to the five human evaluation metrics. In the automatic quantitative evaluation, ERNIE Bot, Tongyi Qianwen, and Claude demonstrated the best overall performance in generating impressions for CT, PET-CT, and US reports, respectively. In the human semantic evaluation, ERNIE Bot outperformed the other LLMs in terms of conciseness, verisimilitude, and replaceability on CT impression generation, while its completeness and correctness scores were comparable to those of other LLMs. Tongyi Qianwen excelled in PET-CT impression generation, with the highest scores for correctness, conciseness, verisimilitude, and replaceability. Claude achieved the best conciseness, verisimilitude, and replaceability scores on US impression generation, and its completeness and correctness scores are close to the best results obtained by other LLMs. The generated impressions were generally complete and correct but lacked conciseness and verisimilitude. Although one-shot and few-shot prompts improved conciseness and verisimilitude, clinicians noted a significant gap between the generated impressions and those written by radiologists. Current LLMs can produce radiology impressions with high completeness and correctness but fall short in conciseness and verisimilitude, indicating they cannot yet fully replace impressions written by radiologists.