Interrater variation in scoring radiological discrepancies.
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ABSTRACT:
SUBMITTER: Mucci B
PROVIDER: S-EPMC3745061 | biostudies-other | 2013 Aug
REPOSITORIES: biostudies-other
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Project description:ObjectivesTo explore whether variations in odontogenesis-related genes are associated with tooth-size discrepancies.Materials and methodsMeasurements of the width of permanent teeth were obtained from dental casts of 62 orthodontic patients (age 15.65 ± 6.82 years; 29 males and 33 females). Participants were classified according to the anterior and overall Bolton ratios as without tooth-size discrepancy or with maxillary or mandibular tooth-size excess. Genomic DNA extracted from buccal cells was used, and 13 single nucleotide polymorphisms (SNPs) across nine genes were genotyped by polymerase chain reaction using TaqMan chemistry. χ2 or Fisher exact tests were applied to determine the overrepresentation of genotypes/alleles depending on the type of tooth-size discrepancy (α = .05; corrected P value: P < 5.556 × 10-3). Odds ratios (ORs) and their correspondent 95% confidence intervals (CIs) were also calculated to investigate the risk of this phenotype for the SNPs having significant association.ResultsIndividuals carrying the FGF10 rs900379 T allele were more likely to have larger mandibular teeth (OR = 3.74; 95% CI: 1.65-8.47; P = .002). This effect appeared to be stronger when two copies of the risk allele (TT) were found (recessive model, OR = 6.16; 95% CI: 1.71-22.16; P = .006). On the other hand, FGF13 rs5931572 rare homozygotes (AA, or male A hemizygotes) had increased risk of displaying tooth-size discrepancies when compared with the common homozygotes (GG, or male G hemizygotes; OR = 10.32; 95% CI: 2.20-48.26; P = .003).ConclusionsThe results suggest that FGF10 and FGF13 may contribute to the presence of tooth-size discrepancies.
| S-EPMC8084460 | biostudies-literature
Project description:BackgroundGeographical variation in health care services challenges the basic principle of fair allocation of health care resources. This study aimed to investigate geographical variation in the use of X-ray, CT, MRI and Ultrasound examinations in Norway, the contribution from public and private institutions, and the impact of accessibility and socioeconomic factors on variation in examination rates.MethodsA nationwide survey of activity in all radiological institutions for the year 2002 was used to compare the rates per thousand of examinations in the counties. The data format was files/printouts where the examinations were recorded according to a code system.ResultsOverall rates per thousand of radiological examinations varied by a factor of 2.4. The use of MRI varied from 170 to 2, and CT from 216 to 56 examinations per 1000 inhabitants. Single MRI examinations (knee, cervical spine and head/brain) ranged high in variation, as did certain other spine examinations. For examination of specific organs, the counties' use of one modality was positively correlated with the use of other modalities. Private institutions accounted for 28% of all examinations, and tended towards performing a higher proportion of single examinations with high variability. Indicators of accessibility correlated positively to variation in examination rates, partly due to the figures from the county of Oslo. Correlations between examination rates and socioeconomic factors were also highly influenced by the figures from this county.ConclusionThe counties use of radiological services varied substantially, especially CT and MRI examinations. A likely cause of the variation is differences in accessibility. The coexistence of public and private institutions may be a source of variability, along with socioeconomic factors. The findings represent a challenge to the objective of equality in access to health care services, and indicate a potential for better allocation of overall health care resources.
| S-EPMC1805434 | biostudies-literature
Project description:Diagnostic data routinely collected for hospital admitted patients and used for case-mix adjustment in care provider comparisons and reimbursement are prone to biases. We aim to measure discrepancies, variations and associated factors in recorded chronic morbidities for hospital admitted patients in New South Wales (NSW), Australia. Of all admissions between July 2010 and June 2014 in all NSW public and private acute hospitals, admissions with over 24 hours stay and one or more of the chronic conditions of diabetes, smoking, hepatitis, HIV, and hypertension were included. The incidence of a non-recorded chronic condition in an admission occurring after the first admission with a recorded chronic condition (index admission) was considered as a discrepancy. Poisson models were employed to (i) derive adjusted discrepancy incidence rates (IR) and rate ratios (IRR) accounting for patient, admission, comorbidity and hospital characteristics and (ii) quantify variation in rates among hospitals. The discrepancy incidence rate was highest for hypertension (51% of 262,664 admissions), followed by hepatitis (37% of 12,107), smoking (33% of 548,965), HIV (27% of 1500) and diabetes (19% of 228,687). Adjusted rates for all conditions declined over the four-year period; with the sharpest drop of over 80% for diabetes (47.7% in 2010 vs. 7.3% in 2014), and 20% to 55% for the other conditions. Discrepancies were more common in private hospitals and smaller public hospitals. Inter-hospital differences were responsible for 1% (HIV) to 9.4% (smoking) of variation in adjusted discrepancy incidences, with an increasing trend for diabetes and HIV. Chronic conditions are recorded inconsistently in hospital administrative datasets, and hospitals contribute to the discrepancies. Adjustment for patterns and stratification in risk adjustments; and furthermore longitudinal accumulation of clinical data at patient level, refinement of clinical coding systems and standardisation of comorbidity recording across hospitals would enhance accuracy of datasets and validity of case-mix adjustment.
| S-EPMC4726608 | biostudies-literature
Project description:PurposeWe aimed to characterize novel coronavirus infections based on imaging [chest X-ray and chest computed tomography (CT)] at the time of admission.Materials and methodsWe extracted data from 396 patients with laboratory-confirmed COVID-19 who were managed at 68 hospitals in Japan from January 25 to September 2, 2020. Case patients were categorized as severe (death or treatment with invasive ventilation during hospitalization) and non-severe groups. The imaging findings of the groups were compared by calculating odds ratios (ORs) and 95% confidence intervals (95% CIs), adjusted for sex, age, and hospital size (and radiographic patient positioning for cardiomegaly). Chest X-ray and CT scores ranged from 0 to 72 and 0 to 20, respectively. Optimal cut-off values for these scores were determined by a receiver-operating characteristic (ROC) curve analysis.ResultsThe median age of the 396 patients was 48 years (interquartile range 28-65) and 211 (53.3%) patients were male. Thirty-two severe cases were compared to 364 non-severe cases. At the time of admission, abnormal lesions on chest X-ray and CT were mainly observed in the lower zone/lobe. Among severe cases, abnormal lesions were also seen in the upper zone/lobe. After adjustment, the total chest X-ray and CT score values showed a dose-dependent association with severe disease. For chest X-ray scores, the area under the ROC curve (AUC) was 0.91 (95% CI = 0.86-0.97) and an optimal cut-off value of 9 points predicted severe disease with 83.3% sensitivity and 84.7% specificity. For chest CT scores, the AUC was 0.94 (95% CI = 0.89-0.98) and an optimal cut-off value of 11 points predicted severe disease with 90.9% sensitivity and 82.2% specificity. Cardiomegaly was strongly associated with severe disease [adjusted OR = 24.6 (95% CI = 3.7-166.0)].ConclusionChest CT and X-ray scores and the identification of cardiomegaly could be useful for classifying severe COVID-19 on admission.
| S-EPMC9315080 | biostudies-literature
Project description:BackgroundSince the transfer and application of modern sequencing technologies to the analysis of amplified fragment-length polymorphisms (AFLP), evolutionary biologists have included an increasing number of samples and markers in their studies. Although justified in this context, the use of automated scoring procedures may result in technical biases that weaken the power and reliability of further analyses.ResultsUsing a new scoring algorithm, RawGeno, we show that scoring errors--in particular "bin oversplitting" (i.e. when variant sizes of the same AFLP marker are not considered as homologous) and "technical homoplasy" (i.e. when two AFLP markers that differ slightly in size are mistakenly considered as being homologous)--induce a loss of discriminatory power, decrease the robustness of results and, in extreme cases, introduce erroneous information in genetic structure analyses. In the present study, we evaluate several descriptive statistics that can be used to optimize the scoring of the AFLP analysis, and we describe a new statistic, the information content per bin (Ibin) that represents a valuable estimator during the optimization process. This statistic can be computed at any stage of the AFLP analysis without requiring the inclusion of replicated samples. Finally, we show that downstream analyses are not equally sensitive to scoring errors. Indeed, although a reasonable amount of flexibility is allowed during the optimization of the scoring procedure without causing considerable changes in the detection of genetic structure patterns, notable discrepancies are observed when estimating genetic diversities from differently scored datasets.ConclusionOur algorithm appears to perform as well as a commercial program in automating AFLP scoring, at least in the context of population genetics or phylogeographic studies. To our knowledge, RawGeno is the only freely available public-domain software for fully automated AFLP scoring, from electropherogram files to user-defined working binary matrices. RawGeno was implemented in an R CRAN package (with an user-friendly GUI) and can be found at http://sourceforge.net/projects/rawgeno.
| S-EPMC2656475 | biostudies-literature
Project description:BackgroundThis study aimed to identify predictive factors for risky discrepancies in the emergency department (ED) by analyzing patient recalls associated with resident-to-attending radiology report discrepancies (RRDs).ResultsThis retrospective study analyzed 759 RRDs in computed tomography (CT) and magnetic resonance imaging and their outcomes from 2013 to 2021. After excluding 73 patients lost to follow-up, we included 686 records in the final analysis. Risky discrepancies were defined as RRDs resulting in (1) inpatient management (hospitalization) and (2) adverse outcomes (delayed operations, 30-day in-hospital mortality, or intensive care unit admission). Predictors of risky discrepancies were assessed using multivariable logistic regression analysis. The overall RRD rate was 0.4% (759 of 171,419). Of 686 eligible patients, 21.4% (147 of 686) received inpatient management, and 6.0% (41 of 686) experienced adverse outcomes. RRDs with neurological diseases were associated with the highest ED revisit rate (79.4%, 81 of 102) but not with risky RRDs. Predictive factors of inpatient management were critical finding (odds ratio [OR], 5.60; p < 0.001), CT examination (OR, 3.93; p = 0.01), digestive diseases (OR, 2.54; p < 0.001), and late finalized report (OR, 1.65; p = 0.02). Digestive diseases (OR, 6.14; p = 0.006) were identified as the only significant predictor of adverse outcomes.ConclusionsRisky RRDs were associated with several factors, including CT examination, digestive diseases, and late finalized reports, as well as critical image findings. This knowledge could aid in determining the priority of discrepancies for the appropriate management of RRDs.
| S-EPMC9167364 | biostudies-literature
Project description:The ideal genetic analysis of family data would include whole genome sequence on all family members. A strategy of combining sequence data from a subset of key individuals with inexpensive, genome-wide association study (GWAS) chip genotypes on all individuals to infer sequence level genotypes throughout the families has been suggested as a highly accurate alternative. This strategy was followed by the Genetic Analysis Workshop 18 data providers. We examined the quality of the imputation to identify potential consequences of this strategy by comparing discrepancies between GWAS genotype calls and imputed calls for the same variants. Overall, the inference and imputation process worked very well. However, we find that discrepancies occurred at an increased rate when imputation was used to infer missing data in sequenced individuals. Although this may be an artifact of this particular instantiation of these analytic methods, there may be general genetic or algorithmic reasons to avoid trying to fill in missing sequence data. This is especially true given the risk of false positives and reduction in power for family-based transmission tests when founders are incorrectly imputed as heterozygotes. Finally, we note a higher rate of discrepancies when unsequenced individuals are inferred using sequenced individuals from other pedigrees drawn from the same admixed population.
| S-EPMC4143754 | biostudies-literature
Project description:An aberrant artery (AA) can frequently be observed coursing through the fissure for the ligamentum venosum (FLV) which was termed the "vessel through strait" sign (VTSS) by us. Fundamental data including the incidence, anatomical composition and clinical significance of VTSS and the AAs composing VTSS are still lacking. We sought to give a systematic demonstration on this issue in the present study. VTSS was respectively analyzed in 2,275 patients and was observed in 357 of them. Interestingly, 319 (89.4%) out of the 357 patients exhibiting VTSS were proved to have left hepatic artery variation (LHAV) (247 with replaced left hepatic artery, 64 with accessory left hepatic artery and 8 with variant common hepatic artery). We therefore hypothesized that VTSS could be a sign that strongly associated with LHAV and could be used for its diagnosis. In the following validating analysis, VTSS gained a sensitivity of 96.3% and a specificity of 98.3% for the diagnosis of LHAV in another bicenter cohort consisted of 1,329 patients. In conclusion, VTSS is a signature radiological sign of LHAV which could be used as an easy and specific method for the diagnosis of LHAV.
| S-EPMC4819219 | biostudies-literature
Project description:IntroductionThe identification of metabolomic biomarkers predictive of cancer patient response to therapy and of disease stage has been pursued as a "holy grail" of modern oncology, relying on the metabolic dysfunction that characterizes cancer progression. In spite of the evaluation of many candidate biomarkers, however, determination of a consistent set with practical clinical utility has proven elusive.ObjectiveIn this study, we systematically examine the combined role of data pre-treatment and imputation methods on the performance of multivariate data analysis methods and their identification of potential biomarkers.MethodsUniquely, we are able to systematically evaluate both unsupervised and supervised methods with a metabolomic data set obtained from patient-derived lung cancer core biopsies with true missing values. Eight pre-treatment methods, ten imputation methods, and two data analysis methods were applied in combination.ResultsThe combined choice of pre-treatment and imputation methods is critical in the definition of candidate biomarkers, with deficient or inappropriate selection of these methods leading to inconsistent results, and with important biomarkers either being overlooked or reported as a false positive. The log transformation appeared to normalize the original tumor data most effectively, but the performance of the imputation applied after the transformation was highly dependent on the characteristics of the data set.ConclusionThe combined choice of pre-treatment and imputation methods may need careful evaluation prior to metabolomic data analysis of human tumors, in order to enable consistent identification of potential biomarkers predictive of response to therapy and of disease stage.
| S-EPMC8138701 | biostudies-literature
Project description:AimsMutation detection accuracy has been described extensively; however, it is surprising that pre-PCR processing of formalin-fixed paraffin-embedded (FFPE) samples has not been systematically assessed in clinical context. We designed a RING trial to (i) investigate pre-PCR variability, (ii) correlate pre-PCR variation with EGFR/BRAF mutation testing accuracy and (iii) investigate causes for observed variation.Methods13 molecular pathology laboratories were recruited. 104 blinded FFPE curls including engineered FFPE curls, cell-negative FFPE curls and control FFPE tissue samples were distributed to participants for pre-PCR processing and mutation detection. Follow-up analysis was performed to assess sample purity, DNA integrity and DNA quantitation.ResultsRate of mutation detection failure was 11.9%. Of these failures, 80% were attributed to pre-PCR error. Significant differences in DNA yields across all samples were seen using analysis of variance (p<0.0001), and yield variation from engineered samples was not significant (p=0.3782). Two laboratories failed DNA extraction from samples that may be attributed to operator error. DNA extraction protocols themselves were not found to contribute significant variation. 10/13 labs reported yields averaging 235.8 ng (95% CI 90.7 to 380.9) from cell-negative samples, which was attributed to issues with spectrophotometry. DNA measurements using Qubit Fluorometry demonstrated a median fivefold overestimation of DNA quantity by Nanodrop Spectrophotometry. DNA integrity and PCR inhibition were factors not found to contribute significant variation.ConclusionsIn this study, we provide evidence demonstrating that variation in pre-PCR steps is prevalent and may detrimentally affect the patient's ability to receive critical therapy. We provide recommendations for preanalytical workflow optimisation that may reduce errors in down-stream sequencing and for next-generation sequencing library generation.
| S-EPMC4316935 | biostudies-literature