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  1. 5721
    por Maguire, Sarah, Maloney, Danielle
    Publicado 2021
    “…BACKGROUND: It seems to be a truth universally acknowledged that pathways to care for people with eating disorders are inconsistent and difficult to navigate. …”
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    The implementation of large-scale health system reform in identification, access and treatment of eating disorders in Australia
  2. 5722
    “…We included ‘oracle’ models in the simulation study in which the complexity parameter was chosen based on the true event probabilities (prediction oracle) or regression coefficients (explanation oracle) to demonstrate the capability of ridge regression if truth was known. RESULTS: Performance of ridge regression strongly depends on the choice of complexity parameter. …”
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    To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets
  3. 5723
    “…An introduced postprocessing algorithm for determining maximum diameter was used on both the predicted AAA segmentation and ground truth segmentation, predicting on average an absolute diameter error of 2.3 ± 2.0 mm by 1.4 ± 1.7 mm for each measurement, respectively. …”
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    Machine deep learning accurately detects endoleak after endovascular abdominal aortic aneurysm repair
  4. 5724
    “…Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth [Formula: see text] from a metabolic system on 22 young healthy adults. …”
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    Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
  5. 5725
    “…We proposed a deep learning–based method, which receives inputs from registered 3-mm × 3-mm ICP and DCP angiograms with proper sampling density as the ground truth reference to reconstruct 6-mm × 6-mm high-resolution ICP and DCP en face OCTA. …”
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    An Open-Source Deep Learning Network for Reconstruction of High-Resolution OCT Angiograms of Retinal Intermediate and Deep Capillary Plexuses
  6. 5726
    “…PURPOSE: Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. …”
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    A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
  7. 5727
    “…METHODS: The input data used in this study is composed of both structured and unstructured data. The ground truth labels (CPTs) are obtained from medical coding databases using relative value units which indicates the major operational procedures in each surgery case. …”
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    A tree based approach for multi-class classification of surgical procedures using structured and unstructured data
  8. 5728
    “…The Bland-Altman plots also show the remarkable consistency of our results and the HR derived from the ground-truth ECG. CONCLUSIONS: To the best of our knowledge, this is the first study aimed at improving the robustness of neonatal HR measurement under motion artifacts using an RGB camera. …”
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    Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts
  9. 5729
    “…The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074–0.098 logMAR (within four to five letters), and the root mean square errors were 0.096–0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. …”
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    Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence
  10. 5730
    “…Two thoracic radiologists established the ground truth and 9 test radiologists from Germany and the US independently reviewed all images in 2 sessions (unaided and AI-aided mode) with at least a 1-month washout period. …”
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    An Artificial Intelligence–Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study
  11. 5731
    “…Here, we determine the epigenetic features from publicly available DNase-seq, ATAC-seq, and WGBS data in rice.We benchmark the utility of both epigenetic features separately and in combination, deriving ground-truth from RNA-seq data of infections studies in rice. …”
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    Epigenetic features improve TALE target prediction
  12. 5732
    “…CONCLUSION: Overall, we find that the regional uptake measurements from early-frame (18)F-florbetapir are strongly correlated with regional glucose metabolism as measured in ground-truth (18)F-FDG PET scans, regardless of disease state. …”
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    Regional uptakes from early-frame amyloid PET and (18)F-FDG PET scans are comparable independent of disease state
  13. 5733
    “…RESULTS: On the simulated indications, our algorithm ranked ground truth documents as top 3 for 98% of simple queries and 85% of complex queries. …”
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    Clinical language search algorithm from free-text: facilitating appropriate imaging
  14. 5734
    “…Finally, segmentations from G2 and the deep learning models’ predictions were compared against ground truth labels from G1 and G3, and accuracy, sensitivity, specificity, precision, and F1 scores were computed for images where both segmentations contained tumors. …”
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    A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING
  15. 5735
    por Mourali, Mehdi, Drake, Carly
    Publicado 2022
    “…The latter result is partially explained by a decrease in the perceived objectivity of truth. That is, extended exposure to false claims and debunking attempts appear to weaken the belief that there is an objectively correct answer to how people ought to behave in this situation, which in turn leads to less positive reactions toward masking as the prescribed behavior. …”
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    The Challenge of Debunking Health Misinformation in Dynamic Social Media Conversations: Online Randomized Study of Public Masking During COVID-19
  16. 5736
    “…The prediction of our forecasting system was highly correlated with the ground truth with small relative errors regarding the four geometric properties. …”
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    Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study
  17. 5737
    “…Each dose plane at postirradiation scan intervals of 5 min up to 20 h was then compared to the ground‐truth dose plane using gamma analysis. RESULTS: Gamma pass rates using the TCM at time t, normalized to the pass rate after 20 h postirradiation, were found to have a maximum coefficient of variation of 3% over any postirradiation time. …”
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    A method for time‐independent film dosimetry: Can we obtain accurate patient‐specific QA results at any time postirradiation?
  18. 5738
    “…Read was performed by two blinded neuroradiology residents (R1 and R2). Ground truth was established by an expert neuroradiologist. …”
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    Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients
  19. 5739
    “…Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. …”
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    Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
  20. 5740
    “…RESULTS: According to the Matthews correlation coefficient (MCC), the proposed algorithms reached an overall good correlation with the ground truth (CRM) for AHI <5 vs AHI ≥5 (MCC: 0.4) and AHI <30 vs AHI ≥30 (MCC: 0.3) classifications. …”
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    Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach
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