Conectar
To identify the assessments, diagnostic criteria and outcome measures reported in peer-reviewed literature for children with growing pains and persistent lower limb pain in the presence of restless leg syndrome (RLS).
Scoping review completed in line with Joanna Briggs Institute methodological guidance
Five online databases were searched—MEDLINE, Embase, CINAHL, PsycINFO and AMED—for records up to 14 October 2024.
Records reporting on the use of assessments, diagnostic criteria or outcome measures in children (aged 0–18 years) with growing pains or persistent lower limb pain in the presence of RLS. Articles were required to have a sample size of ≥10 and be available in English language.
Data were extracted by two independent reviewers and analysed using descriptive statistics.
Following review of 19 806 records, 61 unique records were included. Most were observational cross-sectional or case–control designs. Assessments were varied and primarily focused on body functions and pain characteristics rather than activities and participation. There were 15 unique diagnostic criteria reported for growing pains with limited consistency and sometimes conflict between included items. Outcomes measures were only reported in eight records and typically measured pain presence and intensity.
Assessment and subsequent diagnosis of growing pains and persistent pain in the presence of RLS lack consistency. Outcome measures were seldom used as most records were not designed to measure change over time. Standardised practices for assessment and management of these conditions may benefit clinicians and optimise patient care.
by İlhan Uzel, Behrang Ghabchi, Dilşah Çoğulu
IntroductionSupernumerary teeth are a common developmental anomaly in pediatric patients, potentially leading to complications such as impaction, crowding, and delayed eruption. Accurate and early detection is critical to prevent these sequelae and guide appropriate intervention strategies. This study aims to evaluate the diagnostic accuracy and clinical applicability of a convolutional neural networks-based deep learning model (YOLOv8) for the automated localization and binary classification of supernumerary teeth on pediatric panoramic radiographs.
Materials and methodsA retrospective analysis was conducted on 2000 pediatric panoramic radiographs following ethical approval. Three calibrated pediatric dentists independently examined the dataset and annotated a representative subset of 140 radiographs (71 positive, 69 negative), achieving substantial inter-rater agreement (Cohen’s κ = 0.92). Performance was assessed in two stages: (1) segmentation of supernumerary teeth and (2) binary classification of radiographs. An independent validation set of 20 radiographs was used for secondary evaluation. Evaluation metrics included precision, recall, F1-score, and McNemar’s test to compare model predictions with expert labelling.
ResultsThe mean age of the patients was 9.6 ± 2.3 years; 52% were male, 48% were female. The segmentation model yielded 100% precision, 38% recall, and an F1-score of 55%, indicating strong localization when detections were made but limited sensitivity. The classification model achieved 100% accuracy, precision, recall, and F1-score on both internal and external datasets. McNemar’s test revealed no statistically significant discrepancy between the model and expert decisions (p > 0.05). The segmentation model demonstrated high precision in localizing supernumerary teeth; however, recall performance was more modest, indicating occasional under-detection. Due to the limited validation sample size, these findings should be interpreted with caution.
ConclusionsThe YOLOv8-based pipeline demonstrated robust diagnostic accuracy in classifying panoramic radiographs for supernumerary teeth and promising but preliminary results in lesion-level segmentation. These findings highlight the potential utility of advanced deep learning systems in augmenting early diagnosis and streamlining pediatric dental radiology workflows.
To assess the relation of exposure to cement dust and heavy metal (aluminium, cadmium and lead) exposures to pulmonary function among male cement plant workers. The study also aimed to evaluate dose–response relationships and prevalence and severity of respiratory symptoms among exposure categories compared with a control group.
Cross-sectional study.
Secondary-level occupational health clinic in Ankara, Türkiye.
461 male non-smoking cement plant employees were included in total. Participants were categorised into packaging (n=101), milling (n=162) and office unexposed workers (n=198). Inclusion criteria were more than 70% work history in the cement industry and exclusion of pre-existing respiratory disease and missing data from the participants.
Not applicable.
Pulmonary function tests (forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), FEV1/FVC and peak expiratory flow (PEF)) and urinary, cadmium and blood lead concentrations were measured. Lung function impairment was the primary outcome measure; secondary outcomes included metal exposure—pulmonary measure correlations.
Significant negative correlations existed between FEV1 and urine aluminium (r=–0.622, p
Occupational cement dust and heavy metal exposure is closely linked to impaired pulmonary function in cement plant employees, particularly those who work in higher exposure jobs. The implications are robust endorsement of targeted monitoring and preventive interventions. Long-term longitudinal research is necessary to identify long-term outcome and efficacy of exposure reduction approaches.
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