Dataset Information

Development and validation of a falls-grading scale.

ABSTRACT: The recording of fall events is usually subjective and imprecise, which limits clinical practice and falls-related research. We sought to develop and validate a scale to grade near-fall and fall events on the basis of their severity represented by the use of health care resources, with the goal of standardizing fall reporting in the clinical and research settings.Qualitative instrument development was based on a literature review and semistructured interviews to assess face and content validity. We queried older individuals and health care professionals with expertise in the care of patients at risk of falling about clinically important differences to detect and how to optimize the scale's ease of use. To assess the scale's interrater reliability, we created 30 video-vignettes of falls and compared how health care professionals and volunteers rated each of the falls according to our grading scale.We developed the illustrated 4-point Hopkins Falls Grading Scale (HFGS). The grades distinguish a near-fall (grade 1) from a fall for which an individual did not receive medical attention (grade 2), a fall associated with medical attention but not hospital admission (grade 3), and a fall associated with hospital admission (grade 4). Overall, the HFGS exhibited good face and content validity and had an intraclass correlation coefficient of 0.998.The 4-point HFGS demonstrates good face and content validity and high interrater reliability. We predict that this tool will facilitate the standardization of falls reporting in both the clinical and research settings.

SUBMITTER: Davalos-Bichara M

PROVIDER: S-EPMC3867809 | biostudies-literature | 2013 Apr-Jun

REPOSITORIES: biostudies-literature

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Development and validation of a falls-grading scale.

Davalos-Bichara Marcela M Lin Frank R FR Carey John P JP Walston Jeremy D JD Fairman Jennifer E JE Schubert Michael C MC Barron Jeremy S JS Hughes Jennifer J Millar Jennifer L JL Spar Anne A Weber Kristy L KL Ying Howard S HS Zackowski Kathleen M KM Zee David S DS Agrawal Yuri Y

Journal of geriatric physical therapy (2001) 20130401 2

<h4>Background and purpose</h4>The recording of fall events is usually subjective and imprecise, which limits clinical practice and falls-related research. We sought to develop and validate a scale to grade near-fall and fall events on the basis of their severity represented by the use of health care resources, with the goal of standardizing fall reporting in the clinical and research settings.<h4>Methods</h4>Qualitative instrument development was based on a literature review and semistructured ...[more]

PMID: 22810170

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Project description:BackgroundThe family environment is important for explaining individual health behaviour. While previous research mostly focused on influences among family members and dyadic interactions (parent-child), the purpose of this study was to develop a new measure, the Family Health Climate Scale (FHC-Scale), using a family-based approach. The FHC is an attribute of the whole family and describes an aspect of the family environment that is related to health and health behaviour. Specifically, a questionnaire measuring the FHC (a) for nutrition (FHC-NU) and (b) for activity behaviour (FHC-PA) was developed and validated.MethodsIn Study 1 (N=787) the FHC scales were refined and validated. The sample was randomly divided into two subsamples. With random sample I exploratory factor analyses were conducted and items were selected according to their psychometric quality. In a second step, confirmatory factor analyses were conducted using the random sample II. In Study 2 (N=210 parental couples) the construct validity was tested by correlating the FHC to self-determined motivation of healthy eating and physical activity as well as the families' food environment and joint physical activities.ResultsExploratory factor analyses with random sample I (Study 1) revealed a four (FHC-NU) and a three (FHC-PA) factor model. These models were cross-validated with random sample II and demonstrated an acceptable fit [FHC-PA: χ(2)=222.69, df=74, p<.01; χ(2)/df=3.01; CFI=.96; SRMR=.04; RMSEA=.07, CI .06/.08; FHC-NU: χ(2)=278.30, df=113, p<.01, χ(2)/df=2.46, CFI=.96; SRMR=.04; RMSEA=.06, CI .05/.07]. The perception of FHC correlated (p<.01) with the intrinsic motivation of healthy eating (r=.42) and physical activity (r=.56). Moreover, parental perceptions of FHC-NU correlated with household soft drink availability (r=-.31) and perceptions of FHC-PA with the frequency of joint physical activities with the child (r=.51). These patterns were found on the intraindividual and interindividual level.ConclusionsTwo valid instruments measuring the FHC within families were developed. The use of different informants' ratings demonstrated that the FHC is a family level variable. The results confirm the high relevance of the FHC for individuals' health behaviour. The FHC and the measurement instruments are useful for examining health-related aspects of the family environment.

Project description:BackgroundThe management of acne requires the consideration of its severity; however, a universally adopted evaluation system for clinical practice is lacking. Artificial intelligence (AI) evaluation systems hold the promise of enhancing the efficiency and reproducibility of assessments. Artificial intelligence (AI) evaluation systems offer the potential to enhance the efficiency and reproducibility of assessments in this domain. While the identification of skin lesions represents a crucial component of acne evaluation, existing AI systems often overlook lesion identification or fail to integrate it with severity assessment. This study aimed to develop an AI-powered acne grading system and compare its performance with physician image-based scoring.MethodsA total of 1,501 acne patients were included in the study, and standardized pictures were obtained using the VISIA system. The initial evaluation involved 40 stratified sampled frontal photos assessed by seven dermatologists. Subsequently, the three doctors with the highest inter-rater agreement annotated the remaining 1,461 images, which served as the dataset for the development of the AI system. The dataset was randomly divided into two groups: 276 images were allocated for training the acne lesion identification platform, and 1,185 images were used to assess the severity of acne.ResultsThe average precision of our model for skin lesion identification was 0.507 and the average recall was 0.775. The AI severity grading system achieved good agreement with the true label (linear weighted kappa = 0.652). After integrating the lesion identification results into the severity assessment with fixed weights and learnable weights, the kappa rose to 0.737 and 0.696, respectively, and the entire evaluation on a Linux workstation with a Tesla K40m GPU took less than 0.1s per picture.ConclusionThis study developed a system that detects various types of acne lesions and correlates them well with acne severity grading, and the good accuracy and efficiency make this approach potentially an effective clinical decision support tool.

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Project description:ImportanceThe Gleason grading system has been the most reliable tool for the prognosis of prostate cancer since its development. However, its clinical application remains limited by interobserver variability in grading and quantification, which has negative consequences for risk assessment and clinical management of prostate cancer.ObjectiveTo examine the impact of an artificial intelligence (AI)-assisted approach to prostate cancer grading and quantification.Design, setting, and participantsThis diagnostic study was conducted at the University of Wisconsin-Madison from August 2, 2017, to December 30, 2019. The study chronologically selected 589 men with biopsy-confirmed prostate cancer who received care in the University of Wisconsin Health System between January 1, 2005, and February 28, 2017. A total of 1000 biopsy slides (1 or 2 slides per patient) were selected and scanned to create digital whole-slide images, which were used to develop and validate a deep convolutional neural network-based AI-powered platform. The whole-slide images were divided into a training set (n = 838) and validation set (n = 162). Three experienced academic urological pathologists (W.H., K.A.I., and R.H., hereinafter referred to as pathologists 1, 2, and 3, respectively) were involved in the validation. Data were collected between December 29, 2018, and December 20, 2019, and analyzed from January 4, 2020, to March 1, 2021.Main outcomes and measuresAccuracy of prostate cancer detection by the AI-powered platform and comparison of prostate cancer grading and quantification performed by the 3 pathologists using manual vs AI-assisted methods.ResultsAmong 589 men with biopsy slides, the mean (SD) age was 63.8 (8.2) years, the mean (SD) prebiopsy prostate-specific antigen level was 10.2 (16.2) ng/mL, and the mean (SD) total cancer volume was 15.4% (20.1%). The AI system was able to distinguish prostate cancer from benign prostatic epithelium and stroma with high accuracy at the patch-pixel level, with an area under the receiver operating characteristic curve of 0.92 (95% CI, 0.88-0.95). The AI system achieved almost perfect agreement with the training pathologist (pathologist 1) in detecting prostate cancer at the patch-pixel level (weighted κ = 0.97; asymptotic 95% CI, 0.96-0.98) and in grading prostate cancer at the slide level (weighted κ = 0.98; asymptotic 95% CI, 0.96-1.00). Use of the AI-assisted method was associated with significant improvements in the concordance of prostate cancer grading and quantification between the 3 pathologists (eg, pathologists 1 and 2: 90.1% agreement using AI-assisted method vs 84.0% agreement using manual method; P < .001) and significantly higher weighted κ values for all pathologists (eg, pathologists 2 and 3: weighted κ = 0.92 [asymptotic 95% CI, 0.90-0.94] for AI-assisted method vs 0.76 [asymptotic 95% CI, 0.71-0.80] for manual method; P < .001) compared with the manual method.Conclusions and relevanceIn this diagnostic study, an AI-powered platform was able to detect, grade, and quantify prostate cancer with high accuracy and efficiency and was associated with significant reductions in interobserver variability. These results suggest that an AI-powered platform could potentially transform histopathological evaluation and improve risk stratification and clinical management of prostate cancer.

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Development and validation of a falls-grading scale.

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Development and validation of a falls-grading scale.

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