False Positive Hoover's Sign in Apraxia.
Ontology highlight
ABSTRACT:
SUBMITTER: Ercoli T
PROVIDER: S-EPMC7328420 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
False Positive Hoover's Sign in Apraxia.
Movement disorders clinical practice 20200521 5
Similar Datasets
Project description: Not available
| S-EPMC11322587 | biostudies-literature
Project description:PurposeManual sign is a common alternative mode of communication taught to children with childhood apraxia of speech (CAS). Gesture use is positively related to later increases in vocabulary and syntactic complexity in typical development, but there is little evidence supporting the use of manual sign for children with CAS. We sought to identify the communicative functions of signs and gestures produced by children with CAS and to identify concurrent factors suggesting which children are more likely to benefit from sign-supported speech intervention.MethodMeasures of receptive and expressive language were gathered from 19 children (ages 3.8-11.1 years) with CAS in a school-based sign-supported speech program. Fourteen of the children produced a total of 145 manual signs, which included both gestures and signs from American Sign Language (M = 10.4 per child, SD = 11.6). Manual signs were coded according to whether they conveyed information that was semantically redundant with (complemented) or added information to (supplemented) their speech.ResultsChildren produced 107 complementary manual signs (75.4%) and 38 supplemental (24.6%) manual signs. Of the 38 supplemental signs, 24 (63.2%) provided additional information in the presence of unintelligible or no speech and 14 (36.8%) provided additional information in the presence of intelligible speech. Children's expressive language scores significantly predicted and accounted for 38.4% of the variance in the number of supplemental signs that children used.ConclusionChildren with CAS whose oral expressive language was relatively more impaired produced the most supplementary signs, suggesting that children with oral expressive language challenges are more likely to rely on them for communicating words they cannot yet speak.Supplemental materialhttps://doi.org/10.23641/asha.21217814.
| S-EPMC9913131 | biostudies-literature
Project description:BackgroundIn medical research, null hypothesis significance testing (NHST) is the dominant framework for statistical inference. NHST involves calculating P-values and confidence intervals to quantify the evidence against the null hypothesis of no effect. However, P-values and confidence intervals cannot tell us the probability that the hypothesis is true. In contrast, false-positive risk (FPR) and false-negative risk (FNR) are post-test probabilities concerning the truth of the hypothesis, that is to say, the probability a real effect exists.MethodsWe calculated the FPR or FNR for 53 individual multicentre trials in critical care based on a pretest probability of 0.5 that the hypothesis was true.ResultsFor trials reporting statistical significance, the FPR varied between 0.1% and 57.6%. For trials reporting non-significance, the FNR varied between 1.7% and 36.9%. Twenty-six of 47 trials (55.3%) reporting non-significance provided strong or very strong evidence in favour of the null hypothesis; the remaining trials provided limited evidence. There was no obvious relationship between the P-value and the FNR.ConclusionsThe FPR and FNR showed marked variability, indicating that the probability of a real or absent treatment effect differed substantially between trials. Only one trial reporting statistical significance provided convincing evidence of a real treatment effect, and nearly half of all trials reporting non-significance provided limited evidence for the absence of a treatment effect. Our findings suggest that the quality of evidence from multicentre trials in critical care is highly variable.
| S-EPMC10430847 | biostudies-literature
Project description:Disease gene discovery has been transformed by affordable sequencing of exomes and genomes. Identification of disease-causing mutations requires sifting through a large number of sequence variants. A subset of the variants are unlikely to be good candidates for disease causation based on one or more of the following criteria: (1) being located in genomic regions known to be highly polymorphic, (2) having characteristics suggesting assembly misalignment, and/or (3) being labeled as variants based on misleading reference genome information. We analyzed exome sequence data from 118 individuals in 29 families seen in the NIH Undiagnosed Diseases Program (UDP) to create lists of variants and genes with these characteristics. Specifically, we identified several groups of genes that are candidates for provisional exclusion during exome analysis: 23,389 positions with excess heterozygosity suggestive of alignment errors and 1,009 positions in which the hg18 human genome reference sequence appeared to contain a minor allele. Exclusion of such variants, which we provide in supplemental lists, will likely enhance identification of disease-causing mutations using exome sequence data.
| S-EPMC3302978 | biostudies-literature
Project description:Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 2016 have become a large topic within (and outside) neuroimaging. They concluded that existing parametric methods for determining statistically significant clusters had greatly inflated FPRs ("up to 70%," mainly due to the faulty assumption that the noise spatial autocorrelation function is Gaussian shaped and stationary), calling into question potentially "countless" previous results; in contrast, nonparametric methods, such as their approach, accurately reflected nominal 5% FPRs. They also stated that AFNI showed "particularly high" FPRs compared to other software, largely due to a bug in 3dClustSim. We comment on these points using their own results and figures and by repeating some of their simulations. Briefly, while parametric methods show some FPR inflation in those tests (and assumptions of Gaussian-shaped spatial smoothness also appear to be generally incorrect), their emphasis on reporting the single worst result from thousands of simulation cases greatly exaggerated the scale of the problem. Importantly, FPR statistics depends on "task" paradigm and voxelwise p value threshold; as such, we show how results of their study provide useful suggestions for FMRI study design and analysis, rather than simply a catastrophic downgrading of the field's earlier results. Regarding AFNI (which we maintain), 3dClustSim's bug effect was greatly overstated-their own results show that AFNI results were not "particularly" worse than others. We describe further updates in AFNI for characterizing spatial smoothness more appropriately (greatly reducing FPRs, although some remain >5%); in addition, we outline two newly implemented permutation/randomization-based approaches producing FPRs clustered much more tightly about 5% for voxelwise p ≤ 0.01.
| S-EPMC5399747 | biostudies-literature
Project description:Errors in botanical surveying are a common problem. The presence of a species is easily overlooked, leading to false-absences; while misidentifications and other mistakes lead to false-positive observations. While it is common knowledge that these errors occur, there are few data that can be used to quantify and describe these errors. Here we characterise false-positive errors for a controlled set of surveys conducted as part of a field identification test of botanical skill. Surveys were conducted at sites with a verified list of vascular plant species. The candidates were asked to list all the species they could identify in a defined botanically rich area. They were told beforehand that their final score would be the sum of the correct species they listed, but false-positive errors counted against their overall grade. The number of errors varied considerably between people, some people create a high proportion of false-positive errors, but these are scattered across all skill levels. Therefore, a person's ability to correctly identify a large number of species is not a safeguard against the generation of false-positive errors. There was no phylogenetic pattern to falsely observed species; however, rare species are more likely to be false-positive as are species from species rich genera. Raising the threshold for the acceptance of an observation reduced false-positive observations dramatically, but at the expense of more false negative errors. False-positive errors are higher in field surveying of plants than many people may appreciate. Greater stringency is required before accepting species as present at a site, particularly for rare species. Combining multiple surveys resolves the problem, but requires a considerable increase in effort to achieve the same sensitivity as a single survey. Therefore, other methods should be used to raise the threshold for the acceptance of a species. For example, digital data input systems that can verify, feedback and inform the user are likely to reduce false-positive errors significantly.
| S-EPMC5437866 | biostudies-literature
Project description:High-field asymmetric waveform ion mobility spectrometry (FAIMS) separates glycopeptides in the gas phase prior to mass spectrometry (MS) analysis, thus offering the potential to analyze glycopeptides without prior enrichment. Several studies have demonstrated the ability of FAIMS to enhance glycopeptide detection but have primarily focused on N-glycosylation. Here, we evaluated FAIMS for O-glycoprotein and mucin-domain glycoprotein analysis using samples of varying complexity. We demonstrated that FAIMS was useful in increasingly complex samples as it allowed for the identification of more glycosylated species. However, during our analyses, we observed a phenomenon called "in FAIMS fragmentation" (IFF) akin to in source fragmentation but occurring during FAIMS separation. FAIMS experiments showed a 2- to 5-fold increase in spectral matches from IFF compared with control experiments. These results were also replicated in previously published data, indicating that this is likely a systemic occurrence when using FAIMS. Our study highlights that although there are potential benefits to using FAIMS separation, caution must be exercised in data analysis because of prevalent IFF, which may limit its applicability in the broader field of O-glycoproteomics.
| S-EPMC10523363 | biostudies-literature
Project description:Minimizing false positives is a critical issue when variant calling as no method is without error. It is common practice to post-process a variant-call file (VCF) using hard filter criteria intended to discriminate true-positive (TP) from false-positive (FP) calls. These are applied on the simple principle that certain characteristics are disproportionately represented among the set of FP calls and that a user-chosen threshold can maximize the number detected. To provide guidance on this issue, this study empirically characterized all false SNP and indel calls made using real Illumina sequencing data from six disparate species and 166 variant-calling pipelines (the combination of 14 read aligners with up to 13 different variant callers, plus four 'all-in-one' pipelines). We did not seek to optimize filter thresholds but instead to draw attention to those filters of greatest efficacy and the pipelines to which they may most usefully be applied. In this respect, this study acts as a coda to our previous benchmarking evaluation of bacterial variant callers, and provides general recommendations for effective practice. The results suggest that, of the pipelines analysed in this study, the most straightforward way of minimizing false positives would simply be to use Snippy. We also find that a disproportionate number of false calls, irrespective of the variant-calling pipeline, are located in the vicinity of indels, and highlight this as an issue for future development.
| S-EPMC8549357 | biostudies-literature
Project description:The false positive rates (FPR) for surface-based group analysis of cortical thickness, surface area, and volume were evaluated for parametric and non-parametric clusterwise correction for multiple comparisons for a range of smoothing levels and cluster-forming thresholds (CFT) using real data under group assignments that should not yield significant results. For whole cortical surface analysis, thickness showed modest inflation in parametric FPRs above the nominal level (10% versus 5%). Surface area and volume FPRs were much higher (20-30%). In the analysis of interhemispheric thickness asymmetries, FPRs were well controlled by parametric correction, but FPRs for surface area and volume asymmetries were still inflated. In all cases, non-parametric permutation adequately controlled the FPRs. It was found that inflated parametric FPRs were caused by violations in the parametric assumptions, namely a heavier-than-Gaussian spatial correlation. The non-Gaussian spatial correlation originates from anatomical features unique to individuals (e.g., a patch of cortex slightly thicker or thinner than average) and is not a by-product of scanning or processing. Thickness performed better than surface area and volume because thickness does not require a Jacobian correction.
| S-EPMC5857431 | biostudies-literature
Project description: Not available
| S-EPMC10722006 | biostudies-literature