Background: Maculopapular cutaneous mastocytosis (MPCM) in children is classified in two variants: (i) monomorphic variant, presenting with the small macules or papules typically seen in adult patients; and (ii) polymorphic variant with larger lesions of variable size and shape, typically seen in children. The definition of polymorphic and monomorphic variants is mostly intuitive, and a validation of this classification has not been done.
Objective: To study interobserver variability in the classification of MPCM in two groups of observers: mastocytosis experts and general dermatologists.
Materials And Methods: Nineteen cases of childhood MPCM were shown blindly, for classification as monomorphic or polymorphic type, to 10 independent observers (eight dermatologists, one allergist and one haematologist) from Europe and North America with a vast experience in the management of paediatric mastocytosis. Also, the same cases were shown on a screen to 129 general dermatologists attending a meeting; their votes were registered by remote controls. The interobserver variability kappa coefficient (with 95% confidence interval) was calculated to measure the reliability of the correlation.
Results: The value of kappa interobserver variability coefficient for the group of 10 experts (95% confidence interval) was 0.39 (0.18-0.63), which is considered as 'fair'. The value of kappa interobserver variability coefficient for the group of 129 general dermatologists (95% confidence interval) was 0.17 (0.06-0.39), which is considered as 'slight'. A complete agreement of all 10 experts was achieved in only four of 19 cases (21.1%) The most voted choice was concordant between the two groups in only 11 of the 19 cases.
Conclusions: We failed to validate the classification system of childhood MPCM in monomorphic and polymorphic types. While the rate of agreement was low for mastocytosis experts, it was nearly the agreement expected by chance in general dermatologists.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/jdv.17345 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
SEPO-FI: Deep-learning based software to calculate fusion index of muscle cells.
Comput Biol Med
January 2025
School of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea. Electronic address:
The fusion index is a critical metric for quantitatively assessing the transformation of in vitro muscle cells into myotubes in the biological and medical fields. Traditional methods for calculating this index manually involve the labor-intensive counting of numerous muscle cell nuclei in images, which necessitates determining whether each nucleus is located inside or outside the myotubes, leading to significant inter-observer variation. To address these challenges, this study proposes a three-stage process that integrates the strengths of pattern recognition and deep-learning to automatically calculate the fusion index.
View Article and Find Full Text PDFHow Reproducible Are the Ultrasound Features of Adenomyosis Defined by the Revised MUSA Consensus?
J Clin Med
January 2025
Derby Fertility Unit, Royal Derby Hospital, University Hospital of Derby and Burton, Derby DE22 3NE, UK.
: The aim of this study is to assess the inter- and intra-observer reproducibility of the identification of direct and indirect ultrasonographic features of adenomyosis as defined by the revised Morphological Uterus Sonographic Assessment (MUSA) consensus (2022). : A cohort of 74 women, aged 18 to 45, were recruited from the recurrent miscarriage and general gynaecology clinic at a university-based fertility centre. All the participants underwent 2D and 3D transvaginal Ultrasound scan (TVS) examination in the late follicular and early luteal phase.
View Article and Find Full Text PDFAccuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis.
Diagnostics (Basel)
January 2025
Center for Musculoskeletal Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany.
The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom checker apps shows better performance in diagnostic accuracy and urgency assessment compared to physicians remains unclear.
View Article and Find Full Text PDFPostoperative accuracy quantification of corrective osteotomies: standardisation of Q3D-CT methodology.
Eur J Trauma Emerg Surg
January 2025
3D-Lab, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands.
Purpose: Currently, no gold standard exists for 3D analysis of virtually planned surgery accuracy postoperatively. The aim of this study was to present a new, validated and standardised methodology for 3D postoperative assessment of surgical accuracy in patients undergoing 3D virtually planned and guided corrective osteotomies.
Methods: All patients who underwent 3D planned corrective osteotomy in 2021-2022 at our center with a postoperative CT were included.
Dual-energy CT in diagnosing sacral fractures: assessment of diagnostic accuracy and intra- and inter-rater reliabilities.
Eur J Trauma Emerg Surg
January 2025
Department of Orthopaedic Surgery, Hyogo Prefectural Nishinomiya Hospital, 13-9, Rokutanji, Nishinomiya, 662-0918, Japan.
Purpose: Evaluating sacral fractures is crucial in fragility fractures of the pelvis. Dual-energy CT (DECT) is considered useful for diagnosing unclear fractures on single-energy CT (SECT). This study aims to investigate the effectiveness of DECT in diagnosing sacral fractures.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!