Primary study · included by 1 review
Tracking of serum 25-hydroxyvitamin D levels during 14 years in a population-based study and during 12 months in an intervention study.
Zheng 2021 · Jorde Rolf, Sneve Monica, Hutchinson Moira, et al. · American journal of epidemiology · 2010
PubMed 20219763doi:10.1093/aje/kwq005Comparative Study; Journal Article; Randomized Controlled Trial; Research Support, Non-U.S. Gov't
Trial PICO
Not extracted yet. Trial-level population, intervention, comparator, outcomes and design will appear here after the trial extraction run. Until then, the reviews below describe the study.
Results
Not extracted yet. Per-trial results (events, means, effect and weight as each review's forest plot reports them) will appear here after the trial extraction run.
Included by
| Outcome this study informs | Pooled effect | Evidence | Certainty |
|---|---|---|---|
Diagnostic accuracy (prevalence of accuracy) AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) | ~80 | 1 study | Ungraded |
Diagnostic accuracy (prevalence of accuracy) AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) | ~76 | NR | Ungraded |
Diagnostic accuracy (prevalence of accuracy) AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) | ~70 | 1 study | Ungraded |
Diagnostic accuracy (prevalence of accuracy) AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) | OR 2.72 [14.65, 14.65] | 14 studies | Ungraded |
1 including review in total.