Aim : The aim of this study was the initiation of systematic data collection so as to improve the capacity for outcome measurement after cleft repair. Also, a clinical audit was done for evaluation of the process and assessment of outcomes of cleft care. Design and Setting : A questionnaire-based survey and outcome assessment was carried out over a 1-year period from March 2008 to February 2009 at the combined outpatient cleft clinic of a tertiary care center in India. Patients and Participants : Data collection (basic demographic and environmental information) was done twice a week throughout the year by students from the Department of Pediatric Dentistry, at the outpatient cleft clinic. Results : A total of 68 completed cleft lip and palate registry forms, from which all the information was available, were analyzed. There was a skewed sex ratio, with a higher preponderance of boys seeking treatment. Of affected males, 19.1% were between 2 and 5 years of age when they first reported to the cleft clinic. Surprisingly, no bilateral clefts of lip, unilateral cleft lip (right) and unilateral cleft lip and palate (right) were observed in girls. Oral health was poor in 74% of patients; among the dental referrals only 26% could be recruited for orthodontics with a reasonably good prognosis. Conclusions : Poverty, illiteracy, and superstitions prevent an average patient from India from receiving multidisciplinary cleft care. This emphasizes the need to create systems that suit the needs of our target patients.

Download full-text PDF

Source
http://dx.doi.org/10.1597/11-079DOI Listing

Publication Analysis

Top Keywords

cleft clinic
12
cleft lip
12
cleft
10
tertiary care
8
care center
8
data collection
8
cleft care
8
outpatient cleft
8
lip palate
8
lip unilateral
8

Similar Publications

Objective: The purpose of this study was to quantify analgesic use following alveolar cleft bone grafting (ABG) utilizing a posterior iliac crest (PIC) donor site.

Design: This is a prospective cohort study of consecutive patients that underwent ABG with PIC in a 10 month period from November 2022 to September 2023.

Setting: Tertiary care free-standing pediatric hospital.

View Article and Find Full Text PDF

Background/aims: When conducting a randomised controlled trial in surgery, it is important to consider surgical learning, where surgeons' familiarity with one, or both, of the interventions increases during the trial. If present, learning may compromise trial validity. We demonstrate a statistical investigation into surgical learning within a trial of cleft palate repair.

View Article and Find Full Text PDF

CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning.

Med Image Comput Comput Assist Interv

October 2024

Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples.

View Article and Find Full Text PDF

This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.

View Article and Find Full Text PDF

Want 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!