The tissue diagnostic instrument.
Ontology highlight
ABSTRACT:
SUBMITTER: Hansma P
PROVIDER: S-EPMC2832056 | biostudies-other | 2009 May
REPOSITORIES: biostudies-other
Similar Datasets
Project description:ObjectiveTo determine the intra-rater and inter-rater reliability of the Sexual Knowledge Picture Instrument (SKPI), a potential diagnostic instrument for young suspected victims of sexual abuse containing three scoring forms, that is, verbal responses, non-verbal reactions and red flags.DesignVideo-recorded SKPI interviews with children with and without suspicion of child sexual abuse were observed and scored by two trained, independent raters. The second rater repeated the assessment 6 weeks after initial rating to evaluate for intra-rater reliability.Subjects78 children aged 3-9 years old were included in the study. 39 of those included had known suspicion of sexual abuse and the other 39 had no suspicion.Main outcome measuresIntra-rater and inter-rater reliability of the scores per study group and in the total sample were assessed by Cohen's kappa and percentage of agreement (POA).ResultsThe median intra-rater Cohen's kappa exceeded 0.90 and the POA exceeded 95 for all three forms in both study groups, except for the red flag form (median Cohen's kappa 0.54 and POA 87 in the suspected group, and 0.84 and 92, respectively, in the total sample). For the verbal scoring form the median inter-rater Cohen's kappa and POA were 1.00 and 100, respectively, in both groups. For the non-verbal form the median inter-rater kappa and POA were 0.37 and 97, respectively, in the suspected group, and 0.47 and 100, respectively, in the control group. For the red flag form, they were 0.37 and 76, respectively, in the suspected group and 0.42 and 77, respectively, in the control group.ConclusionThe reliability of the SKPI verbal form was sufficient, but there is room for improvement in the non-verbal and red flag scoring forms. These forms may be improved by adjusting the manual and improving rater training.
| S-EPMC9280880 | biostudies-literature
Project description:BackgroundThe Provisional Diagnostic Instrument (PDI-4) is a brief, adult self-report instrument for 4 common psychiatric diagnoses in primary care patients: major depressive episode (MDE), generalized anxiety disorder (GAD), attention deficit hyperactivity disorder (ADHD), and bipolar I disorder based on past or present mania. Our objective was to assess validity of the PDI-4 in a population independent of the study population originally used to develop the scale.MethodsAn online version of the 17-item PDI-4 was administered to 1,047 adults in the US; respondents also completed the PHQ-9, HADS-A, CAARS-S, and MDQ within the online survey. Respondents self-reported diagnosis by a healthcare professional with the terms depression (n=221), anxiety (n=218), attention deficit disorder (n=206), bipolar or manic depressive disorder (n=195), or none of these (n=207). Statistical analyses examined convergent and discriminant validity, and operating characteristics of the PDI-4 relative to the individual, validated, self-rated scales PHQ-9, HADS-A, CAARS-S, and MDQ, for each PDI-4 diagnosis.ResultsConvergent validity of the PDI-4 was supported by strong correlations with the corresponding individual scales (range of 0.63 [PDI-4 and MDQ] to 0.87 [PDI-4 and PHQ-9]). Operating characteristics of the PDI-4 were similar to results in the previous site-based study. The scale exhibited moderate sensitivities (0.52 [mania] to 0.70 [ADHD]) and strong specificities (0.86 [mania] to 0.92 [GAD]) using the individual scales as the gold standards. ANOVAs demonstrated that PDI-4 discriminated between subsets of patients defined by pre-specified severity level cutoff scores of the individual scales. However, overlapping symptoms and co-morbidities made differentiation between mental diagnoses much weaker than differentiation from the control group with none of the diagnoses.ConclusionsThe PDI-4 appears to be a suitable, brief, self-rated tool for provisional diagnoses of common mental disorders. However, the high level of symptom overlap between these diagnoses emphasizes that such brief scales are not a replacement for thorough diagnostic evaluation by trained medical providers.
| S-EPMC3515462 | biostudies-literature
Project description:ImportanceDiagnostic errors are common and harmful, but difficult to define and measure. Measurement of diagnostic errors often depends on retrospective medical record reviews, frequently resulting in reviewer disagreement.ObjectivesWe aimed to test the accuracy of an instrument to help detect presence or absence of diagnostic error through record reviews.DesignWe gathered questions from several previously used instruments for diagnostic error measurement, then developed and refined our instrument. We tested the accuracy of the instrument against a sample of patient records (n?=?389), with and without previously identified diagnostic errors (n?=?129 and n?=?260, respectively).ResultsThe final version of our instrument (titled Safer Dx Instrument) consisted of 11 questions assessing diagnostic processes in the patient-provider encounter and a main outcome question to determine diagnostic error. In comparison with the previous sample, the instrument yielded an overall accuracy of 84 %, sensitivity of 71 %, specificity of 90 %, negative predictive value of 86 %, and positive predictive value of 78 %. All 11 items correlated significantly with the instrument's error outcome question (all p values???0.01). Using factor analysis, the 11 questions clustered into two domains with high internal consistency (initial diagnostic assessment, and performance and interpretation of diagnostic tests) and a patient factor domain with low internal consistency (Cronbach's alpha coefficients 0.93, 0.92, and 0.38, respectively).ConclusionsThe Safer Dx Instrument helps quantify the likelihood of diagnostic error in primary care visits, achieving a high degree of accuracy for measuring their presence or absence. This instrument could be useful to identify high-risk cases for further study and quality improvement.
| S-EPMC4870415 | biostudies-literature
Project description:Laboratory testing to support the care of patients with highly infectious diseases may pose a risk for laboratory workers. However, data on the risk of virus transmission during routine laboratory testing conducted using standard personal protective equipment (PPE) are sparse. Our objective was to measure laboratory contamination during routine analysis of patient specimens. Remnant specimens were spiked with the nonpathogenic bacteriophage MS2 at 1.0 × 107 PFU/ml, and contamination was assessed using reverse transcriptase PCR (RT-PCR) for MS2. Specimen containers were exteriorly coated with a fluorescent powder to enable the visualization of gross contamination using UV light. Testing was performed by two experienced laboratory technologists using standard laboratory PPE and sample-to-answer instrumentation. Fluorescence was noted on the gloves, bare hands, and laboratory coat cuffs of the laboratory technologist in 36/36 (100%), 13/36 (36%), and 4/36 (11%) tests performed, respectively. Fluorescence was observed in the biosafety cabinet (BSC) in 8/36 (22%) tests, on test cartridges/devices in 14/32 (44%) tests, and on testing accessory items in 29/32 (91%) tests. Fluorescence was not observed on or in laboratory instrumentation or adjacent surfaces. In contrast to fluorescence detection, MS2 detection was infrequent (3/286 instances [1%]) and occurred during test setup for the FilmArray instrument and on FilmArray accessory equipment. The information from this study may provide opportunities for the improvement of clinical laboratory safety practices so as to reduce the risk of pathogen transmission to laboratory workers.
| S-EPMC5971558 | biostudies-literature
Project description:AbstractThe Michigan Neuropathy Screening Instrument (MNSI) is used to screen patients for diabetic neuropathy (DNP). We aimed to translate the MNSI questionnaire into Arabic (MNSIq-Ar) and to assess the validity and diagnostic performance of the MNSI Arabic version (MNSI-Ar).Cronbach alpha α and the interclass correlation coefficient were used to measure the reliability and reproducibility of the MNSIq-Ar. The instrument's validity was assessed by Spearman correlation with the Utah Early Neuropathy Scale (UENS), the Modified Toronto Neuropathy Score (mTCNS), diabetic neuropathy symptoms (DNS), and sural nerve amplitude (SNA). The construct validity of the MNSI-Ar was assessed by its ability to differentiate the severity of DNP (using the Kruskal-Wallis test). The diagnostic performance was assessed through the receiver operator curve area.We recruited 89 participants (mean [SD] age, 50.8 [12.3] years; 48% men). The MNSIq-Ar showed an α of 0.81 and intraclass correlation coefficient = 0.94, and the correlation coefficients with UENS, mTCNS, DNS, and sural nerve amplitude were 0.67, 0.83, 0.73, and -0.49, respectively (all P < .0001). The MNSI-Ar was able to differentiate the different severities of DNP. The receiver operator curve area was 0.93 with a high sensitivity of 95.9% and 100% for probable and confirmed DNP, respectively.MNSI-Ar is a reliable and valid tool to screen for diabetic neuropathy in the Arabic language with a good diagnostic performance and high sensitivity.
| S-EPMC8568465 | biostudies-literature
Project description: Not available
| S-EPMC10750430 | biostudies-literature
Project description:BackgroundIn January 2003, STAndards for the Reporting of Diagnostic accuracy studies (STARD) were published in a number of journals, to improve the quality of reporting in diagnostic accuracy studies. We designed a study to investigate the inter-assessment reproducibility, and intra- and inter-observer reproducibility of the items in the STARD statement.MethodsThirty-two diagnostic accuracy studies published in 2000 in medical journals with an impact factor of at least 4 were included. Two reviewers independently evaluated the quality of reporting of these studies using the 25 items of the STARD statement. A consensus evaluation was obtained by discussing and resolving disagreements between reviewers. Almost two years later, the same studies were evaluated by the same reviewers. For each item, percentages agreement and Cohen's kappa between first and second consensus assessments (inter-assessment) were calculated. Intraclass Correlation coefficients (ICC) were calculated to evaluate its reliability.ResultsThe overall inter-assessment agreement for all items of the STARD statement was 85% (Cohen's kappa 0.70) and varied from 63% to 100% for individual items. The largest differences between the two assessments were found for the reporting of the rationale of the reference standard (kappa 0.37), number of included participants that underwent tests (kappa 0.28), distribution of the severity of the disease (kappa 0.23), a cross tabulation of the results of the index test by the results of the reference standard (kappa 0.33) and how indeterminate results, missing data and outliers were handled (kappa 0.25). Within and between reviewers, also large differences were observed for these items. The inter-assessment reliability of the STARD checklist was satisfactory (ICC = 0.79 [95% CI: 0.62 to 0.89]).ConclusionAlthough the overall reproducibility of the quality of reporting on diagnostic accuracy studies using the STARD statement was found to be good, substantial disagreements were found for specific items. These disagreements were not so much caused by differences in interpretation of the items by the reviewers but rather by difficulties in assessing the reporting of these items due to lack of clarity within the articles. Including a flow diagram in all reports on diagnostic accuracy studies would be very helpful in reducing confusion between readers and among reviewers.
| S-EPMC1522016 | biostudies-literature
Project description:PurposeTo systematically evaluate and compare the effects of using small-gauge needles and vitrectors on the ability to obtain adequate diagnostic and prognostic uveal melanoma biopsy specimens.DesignComparative evaluation of biopsy instruments.MethodsSurvival of uveal melanoma cells was evaluated in vitro following needle aspiration. Five therapeutically enucleated eyes were sampled in triplicate for ex vivo diagnostic biopsy experiments with 25 gauge (25 G) needle, 27 gauge (27 G) needle, and 27 G vitrector. During surgery in 8 patients, paired diagnostic transscleral fine needle aspiration biopsies were performed using both 25 G and 27 G needles. A review of cytologic specimens was performed by a panel of 3 expert cytopathologists. A retrospective chart review was performed to evaluate 100 consecutive tumors undergoing prognostic biopsy for gene expression profiling to assess the relationship between needle gauge and prognostic adequacy.ResultsNo significant cell shearing of uveal melanoma cells occurred in vitro with 25 G, 27 G, or 30 G needles. For ex vivo biopsy samples, diagnostic yield was 100% using 25 G needle (5/5) or 27 G vitrector (5/5) but 60% using a 27 G needle (3/5). For in vivo samples, no difference in diagnostic yield was found between 25 G (75%, 6/8) or 27 G (75%, 6/8) needle sizes. Of 100 molecular prognostic biopsy samples evaluated, 65 were obtained using 27 G needles; for these biopsies, the prognostic yield was 65/65 (100%).ConclusionsFor diagnostic biopsy of uveal melanoma, a larger-gauge needle or a 27 G vitrector may have better overall cellularity and diagnostic yield when compared to a 27 G needle. However, for much more common molecular prognostic testing, a 27 G needle provided adequate sample in 100% (65/65) of cases, and a larger needle provided no additional benefit.
| S-EPMC8117558 | biostudies-literature
Project description:BackgroundDiabetic retinopathy (DR) is a leading cause of vision loss in working age individuals worldwide. While screening is effective and cost effective, it remains underutilized, and novel methods are needed to increase detection of DR. This clinical validation study compared diagnostic gradings of retinal fundus photographs provided by volunteers on the Amazon Mechanical Turk (AMT) crowdsourcing marketplace with expert-provided gold-standard grading and explored whether determination of the consensus of crowdsourced classifications could be improved beyond a simple majority vote (MV) using regression methods.ObjectiveThe aim of our study was to determine whether regression methods could be used to improve the consensus grading of data collected by crowdsourcing.MethodsA total of 1200 retinal images of individuals with diabetes mellitus from the Messidor public dataset were posted to AMT. Eligible crowdsourcing workers had at least 500 previously approved tasks with an approval rating of 99% across their prior submitted work. A total of 10 workers were recruited to classify each image as normal or abnormal. If half or more workers judged the image to be abnormal, the MV consensus grade was recorded as abnormal. Rasch analysis was then used to calculate worker ability scores in a random 50% training set, which were then used as weights in a regression model in the remaining 50% test set to determine if a more accurate consensus could be devised. Outcomes of interest were the percent correctly classified images, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) for the consensus grade as compared with the expert grading provided with the dataset.ResultsUsing MV grading, the consensus was correct in 75.5% of images (906/1200), with 75.5% sensitivity, 75.5% specificity, and an AUROC of 0.75 (95% CI 0.73-0.78). A logistic regression model using Rasch-weighted individual scores generated an AUROC of 0.91 (95% CI 0.88-0.93) compared with 0.89 (95% CI 0.86-92) for a model using unweighted scores (chi-square P value<.001). Setting a diagnostic cut-point to optimize sensitivity at 90%, 77.5% (465/600) were graded correctly, with 90.3% sensitivity, 68.5% specificity, and an AUROC of 0.79 (95% CI 0.76-0.83).ConclusionsCrowdsourced interpretations of retinal images provide rapid and accurate results as compared with a gold-standard grading. Creating a logistic regression model using Rasch analysis to weight crowdsourced classifications by worker ability improves accuracy of aggregated grades as compared with simple majority vote.
| S-EPMC5497070 | biostudies-literature
Project description:BackgroundMachine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools.MethodsThe data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation.ResultsThe created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes.ConclusionsML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight the limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools.
| S-EPMC4958551 | biostudies-literature