Validity Of Classification Research Articles

Psychometric implications of failure on one performance validity test: a cross-validation study to inform criterion group definition

ABSTRACT Introduction: Research to date has supported the use of multiple performance validity tests (PVTs) for determining validity status in clinical settings. However, the implications of including versus excluding patients failing one PVT remains a source of debate, and methodological guidelines for PVT research are lacking. This study evaluated three validity classification approaches (i.e. 0 vs. ≥2, 0–1 vs. ≥2, and 0 vs. ≥1 PVT failures) using three reference standards (i.e. criterion PVT groupings) to recommend approaches best suited to establishing validity groups in PVT research methodology. Method: A mixed clinical sample of 157 patients was administered freestanding (Medical Symptom Validity Test, Dot Counting Test, Test of Memory Malingering, Word Choice Test), and embedded PVTs (Reliable Digit Span, RAVLT Effort Score, Stroop Word Reading, BVMT-R Recognition Discrimination) during outpatient neuropsychological evaluation. Three reference standards (i.e. two freestanding and three embedded PVTs from the above list) were created. Rey 15-Item Test and RAVLT Forced Choice were used solely as outcome measures in addition to two freestanding PVTs not employed in the reference standard. Receiver operating characteristic curve analyses evaluated classification accuracy using the three validity classification approaches for each reference standard. Results: When patients failing only one PVT were excluded or classified as valid, classification accuracy ranged from acceptable to excellent. However, classification accuracy was poor to acceptable when patients failing one PVT were classified as invalid. Sensitivity/specificity across two of the validity classification approaches (0 vs. ≥2; 0–1 vs. ≥2) remained reasonably stable. Conclusions: These results reflect that both inclusion and exclusion of patients failing one PVT are acceptable approaches to PVT research methodology and the choice of method likely depends on the study rationale. However, including such patients in the invalid group yields unacceptably poor classification accuracy across a number of psychometrically robust outcome measures and therefore is not recommended.

Evidence-Based Assessment of Genes in Dilated Cardiomyopathy.

Each of the cardiomyopathies, classically categorized as hypertrophic cardiomyopathy, dilated cardiomyopathy (DCM), and arrhythmogenic right ventricular cardiomyopathy, has a signature genetic theme. Hypertrophic cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy are largely understood as genetic diseases of sarcomere or desmosome proteins, respectively. In contrast, >250 genes spanning >10 gene ontologies have been implicated in DCM, representing a complex and diverse genetic architecture. To clarify this, a systematic curation of evidence to establish the relationship of genes with DCM was conducted. An international panel with clinical and scientific expertise in DCM genetics evaluated evidence supporting monogenic relationships of genes with idiopathic DCM. The panel used the Clinical Genome Resource semiquantitative gene-disease clinical validity classification framework with modifications for DCM genetics to classify genes into categories on the basis of the strength of currently available evidence. Representation of DCM genes on clinically available genetic testing panels was evaluated. Fifty-one genes with human genetic evidence were curated. Twelve genes (23%) from 8 gene ontologies were classified as having definitive (BAG3, DES, FLNC, LMNA, MYH7, PLN, RBM20, SCN5A, TNNC1, TNNT2, TTN) or strong (DSP) evidence. Seven genes (14%; ACTC1, ACTN2, JPH2, NEXN, TNNI3, TPM1, VCL) including 2 additional ontologies were classified as moderate evidence; these genes are likely to emerge as strong or definitive with additional evidence. Of these 19 genes, 6 were similarly classified for hypertrophic cardiomyopathy and 3 for arrhythmogenic right ventricular cardiomyopathy. Of the remaining 32 genes (63%), 25 (49%) had limited evidence, 4 (8%) were disputed, 2 (4%) had no disease relationship, and 1 (2%) was supported by animal model data only. Of the 16 evaluated clinical genetic testing panels, most definitive genes were included, but panels also included numerous genes with minimal human evidence. In the curation of 51 genes, 19 had high evidence (12 definitive/strong, 7 moderate). It is notable that these 19 genes explain only a minority of cases, leaving the remainder of DCM genetic architecture incompletely addressed. Clinical genetic testing panels include most high-evidence genes; however, genes lacking robust evidence are also commonly included. We recommend that high-evidence DCM genes be used for clinical practice and that caution be exercised in the interpretation of variants in variable-evidence DCM genes.

Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders.

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. The most advanced task of distinguishing PD vs. DD was evaluated with 74.1% balanced accuracy, 86.5% precision and 90.5% recall by Multilayer Perceptrons. Deep-learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle tremor signs with low noise. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers, but it remains challenging for distinguishing similar disorders.

Cross-validation of the Utility of Test of Memory Malingering (TOMM) Cut-offs in a Large Colombian Sample

This study sought to cross-validate the utility of Test of Memory Malingering (TOMM) cut-offs derived in a large Colombian sample with further exploration of the impact of age, education, and Mini-Mental Status Examination (MMSE) scores on TOMM trials. To address these aims, the study examines concordance rates of the TOMM scores by demographics and Hopkins Verbal Learning Test-Revised (HVLT-R) test performance. Additionally, item response theory analysis (IRT) focused on the interaction between demographic variables and the psychometric properties of the TOMM items. Data were collected from 1416 healthy controls (58.6% female; M age = 58.19; M education = 9 years) who completed the TOMM, MMSE, and HVLT-R as part of a comprehensive battery conducted in Spanish. Frequency analysis was used to assess for concordance rates of passing TOMMe10, Trial 1, and Trial 2 scores, further broken down by median split for MMSE, education, and age. Additionally, IRT is used to examine, in detail, the psychometric properties of the TOMM items and relationships to demographic variables. Validity classification differed across the TOMM trials, with 82.8% passing e10, 88.6% Trial 1, and 92.5% Trial 2. When dichotomized by a median split, passing rates across all TOMM trials were significantly different (p ≤ .001) for MMSE scores, education, and age with the largest discrepancy (~ 21%) observed on TOMMe10. IRT confirmed that all TOMM items are easy to answer correctly with Trial 2 items being appreciably easier (difficulty range = − 4.90 to − 1.52) than Trial 1 items (difficulty range = − 6.22 to − 3.50). Age, education, MMSE, and HVLT-R scores all were significantly related to latent trait scores on the TOMM (p < .001). Utilization of the TOMM in Spanish-speaking individuals is warranted, but with caution. The TOMMe10 yielded higher rates of invalid classification when compared to Trial 1 or Trial 2. IRT methods allow examination of whether traditional TOMM trial cut-offs may be appropriate when individuals present with advanced age or low educational attainment.

Wavelet Time Scattering Based Classification of Interictal and Preictal EEG Signals

If it were possible to reliably identify the preictal brain state from dynamical changes in EEG data of epilepsy patients, then the age long problem of actualizing a fully automated closed-loop seizure – warning or seizure-prevention system that is clinically deployable would have been resolved. Accordingly, through feature engineering, a great deal of effort has been invested over the discovery of EEG features or measures that are always indicative of the preictal brain state. However, this has proven to be difficult, time consuming and apparently unsuccessful. Therefore, lately, attention has shifted to feature learning-methods that automatically learn and extracts useful discriminatory features from raw data. This paper studies the efficacy of wavelet time scattering learned EEG features for interictal and preictal EEG classification. Wavelet time scattering network developed in Matlab and two different EEG datasets: CHB-MIT scalp EEG and AES intracranial EEG datasets were used for the study. The learned interictal and preictal EEG features were used to train and evaluate a simple binary support vector machine classifier. Three different classification accuracy results namely ordinary cross validation, true cross validation and test classification accuracy results were reported for the analysis. Mean classification accuracy values of 93.15%, 97.57% and 91.33% were obtained respectively for the scalp EEG while mean classification accuracy values of 98.33%, 100% and 96.73% were obtained respectively for the intracranial EEG. A general comparison showed that the combination of wavelet time scattering learned EEG features and a simple binary support vector machine classifier performed equally or even better than deep convolutional neural networks in EEG classification tasks. Finally, wavelet time scattering has proven to be a very good EEG feature learner and may greatly improve the sensitivity and specificity of seizure prediction algorithms.

Reproducibility and validity of the Functional Communication Classification System for young children with cerebral palsy.

To examine interrater agreement and validity of the Functional Communication Classification System (FCCS) for young children with cerebral palsy (CP) aged 2 or 3 years. Speech-language pathologist (SLP) and parent FCCS ratings for 31 children with CP (aged 2y, n=16; aged 3y, n=15; 18 males, 13 females) were examined for interrater agreement using a weighted Cohen's kappa statistic. Relationships between FCCS (SLP) ratings and: (1) concurrent validity with the Language Use Inventory, a standardized pragmatic assessment for children aged 18 to 47 months, (2) gross motor and fine motor function, (3) associated impairments (visual and intellectual), and (4) primary expressive communication mode were examined using Spearman's correlation coefficients. Almost perfect interrater agreement between SLP and parent FCCS ratings were found (kw =0.94). Correlations with FCCS (SLP) were excellent for pragmatic function (rs =-0.83,p<0.001), intellectual function (rs =0.89, p<0.001), and primary expressive communication mode (rs =0.92,p<0.001). Correlations were good for gross motor function (rs =0.72,p<0.001) and visual impairment (rs =0.70,p<0.001) and fair for fine motor function (rs =0.53,p<0.002). Analysis was unwarranted for epilepsy (n=1 out of 31) and hearing-associated impairments (n=0 out of 31). The FCCS has excellent interrater agreement and validity for communication classification of children with CP aged 2 or 3 years and is highly suitable for surveillance and research purposes. What this paper adds The Functional Communication Classification System (FCCS) is a valid instrument for children with cerebral palsy (CP) aged 2 or 3 years. Excellent agreement exists between speech-language pathologist and parent FCCS ratings. The FCCS has excellent correlation with intelligence, pragmatic function, and primary expressive mode. Stronger correlations with the Gross Motor Function Classification System and vision exist for children aged 2 or 3 years. Weaker correlations with manual ability exist when compared to older children with CP.

The Symptoms-Varices-Pathophysiology classification of pelvic venous disorders: A report of the American Vein & Lymphatic Society International Working Group on Pelvic Venous Disorders

As the importance of pelvic venous disorders (PeVD) has been increasingly recognized, progress in the field has been limited by the lack of a valid and reliable classification instrument. Misleading historical nomenclature, such as the May-Thurner, pelvic congestion, and nutcracker syndromes, often fails to recognize the interrelationship of many pelvic symptoms and their underlying pathophysiology. Based on a perceived need, the American Vein and Lymphatic Society convened an international, multidisciplinary panel charged with the development of a discriminative classification instrument for PeVD. This instrument, the Symptoms-Varices-Pathophysiology (“SVP”) classification for PeVD, includes three domains—Symptoms (S), Varices (V), and Pathophysiology (P), with the pathophysiology domain encompassing the Anatomic (A), Hemodynamic (H), and Etiologic (E) features of the patient's disease. An individual patient's classification is designated as SVPA,H,E. For patients with pelvic origin lower extremity signs or symptoms, the SVP instrument is complementary to and should be used in conjunction with the Clinical-Etiologic-Anatomic-Physiologic (CEAP) classification. The SVP instrument accurately defines the diverse patient populations with PeVD, an important step in improving clinical decision making, developing disease-specific outcome measures and identifying homogenous patient populations for clinical trials.

Dide et les psychoses passionnelles

Derived from the Greek pathos and the Latin passio, the French word passion in used in the Middle Age to designate a suffering and then a violent affection. After the Treatise on passions of Galen, it is only during the 17th century that physicians (like Cureau de la Chambre) and ecclesiastics (Coëffeteau, Senault) write books about the passions. In his Treatise on the passions of the soul (1649), Descartes emphasises a dualist approach body/mind, while Spinoza uses in his Ethics (1677) a monist conception. Pinel recommends to oppose the different passions between them in his “moral treatment” (1800). Esquirol writes a medical thesis on the passions (1805) and describes an affective (or reasoning) monomania (1838). Inside the French classification of chronic delusional states by their themes, Pottier isolates the delusion of “persecuted–persecutors” reasoning (1886). Serieux and Capgras develop its description in their “vindication (revendication) delusional state” (1909). Four years later (1913), the book of Maurice Dide (1873–1944), a French alienist working at Toulouse, is published in the context of the new classification of delusional disorders by “mechanisms” (interpretation, hallucination, imagination) and constitutions. The author describes three great groups of passionate idealists: 1) love, either profane, or religious, but always chaste, sudden, based on an erroneous affective judgement, a fixed inclination, mechanisms of passive contemplation and of admiration; 2) goodness, concerning philanthropists, religious and social reformers (Jean de Leyde, Saint-Simon, Cabet), idealists of the nature (François d’Assise, Rousseau, Tolstoï); 3) beauty and justice, leading to cruelty (the most important chapter). The idealism of the esthete is egocentric, relies on an overdeveloped personality (D’Annunzio) and can reverse in moral and physical cruelty (Sade). The passionate idealism of the justice can also be egocentric: it corresponds with the “vindication (revendication) delusion” (overdeveloped personality, instability, sudden beginning, intellectual excitation, capacity of conviction, dangerous behaviour). On another hand, the altruistic idealism of the justice is either individualistic (regicides, crimes of anarchists), or synthetic, with more moderate personality disorders, but implacable reforming fanaticism (Torquemada, Calvin, Robespierre). At last, Dide studies the most pathological class of “instable pseudo-altruist vindictive” lunatics, which present comorbidity with delusional and mood disorders (Marat). The conclusion of the book emphasizes on the prevalence of disorders of the affective faculties rather than of the intellectual ones and on the intensity of the passions among the idealists. They suffer from a personality disorder rather from a psychotic disorder. In 1921, Clérambault describes the passionate delusions or syndromes, subdivided into “vindication”, erotomania and jealousy ones. He distinguishes erotomania from Dide's passionate idealism of love. Guiraud (1942) tries to maintain the autonomy of the two groups. But French manuals of psychiatry, since Henri Ey (1960) valid Clerambault's classification of passionate psychosis, included into the chronic delusional disorders (paranoia).

Application of a lab-made electronic nose for extra virgin olive oils commercial classification according to the perceived fruitiness intensity

An electronic nose, comprising nine metal oxide sensors, has been built aiming to classify olive oils according to the fruity intensity commercial grade (ripely fruity or light, medium and intense greenly fruity), following the European regulated complementary terminology. The lab-made sensor device was capable to differentiate standard aqueous solutions (acetic acid, cis-3-hexenyl, cis-3-hexen-1-ol, hexanal, 1-hexenol and nonanal) that mimicked positive sensations (e.g., fatty, floral, fruit, grass, green and green leaves attributes) and negative attributes (e.g., sour and vinegary defects), as well as to semi-quantitatively classify them according to the concentration ranges (0.05–2.25 mg/kg). For that, unsupervised (principal component analysis) and supervised (linear discriminant analysis: sensitivity of 92% for leave-one-out cross validation) classification multivariate models were established based on nine or six gas sensors, respectively. It was also showed that the built E-nose allowed differentiating/discriminating (sensitivity of 81% for leave-one-out cross validation) extra virgin olive oils according to the perceived intensity of fruitiness as ripely fruity, light, medium or intense greenly fruity. In conclusion, the gas sensor device could be used as a practical preliminary non-destructive tool for guaranteeing the correctness of olive oil fruitiness intensity labelling.

Mortality in septic patients treated with vitamin C: a systematic meta-analysis

BackgroundSupplementation of vitamin C in septic patients remains controversial despite eight large clinical trials published only in 2020. We aimed to evaluate the evidence on potential effects of vitamin C treatment on mortality in adult septic patients.MethodsData search included PubMed, Web of Science, and the Cochrane Library. A meta-analysis of eligible peer-reviewed studies was performed in accordance with the PRISMA statement. Only studies with valid classifications of sepsis and intravenous vitamin C treatment (alone or combined with hydrocortisone/thiamine) were included.ResultsA total of 17 studies including 3133 patients fulfilled the predefined criteria and were analyzed. Pooled analysis indicated no mortality reduction in patients treated with vitamin C when compared to reference (risk difference − 0.05 [95% CI − 0.11 to − 0.01]; p = 0.08; p for Cochran Q = 0.002; I2 = 56%). Notably, subgroup analyses revealed an improved survival, if vitamin C treatment was applied for 3–4 days (risk difference, − 0.10 [95% CI − 0.19 to − 0.02]; p = 0.02) when compared to patients treated for 1–2 or > 5 days. Also, timing of the pooled mortality assessment indicated a reduction concerning short-term mortality (< 30 days; risk difference, − 0.08 [95% CI − 0.15 to − 0.01]; p = 0.02; p for Cochran Q = 0.02; I2 = 63%). Presence of statistical heterogeneity was noted with no sign of significant publication bias.ConclusionAlthough vitamin C administration did not reduce pooled mortality, patients may profit if vitamin C is administered over 3 to 4 days. Consequently, further research is needed to identify patient subgroups that might benefit from intravenous supplementation of vitamin C.

Machine learning methods for multi-walled carbon nanotubes (MWCNT) genotoxicity prediction.

Multi-walled carbon nanotubes (MWCNTs) are made of multiple single-walled carbon nanotubes (SWCNTs) which are nested inside one another forming concentric cylinders. These nanomaterials are widely used in industrial and biomedical applications, due to their unique physicochemical characteristics. However, previous studies have shown that exposure to MWCNTs may lead to toxicity and some of the physicochemical properties of MWCNTs can influence their toxicological profiles. In silico modelling can be applied as a faster and less costly alternative to experimental (in vivo and in vitro) testing for the hazard characterization of MWCNTs. This study aims at developing a fully validated predictive nanoinformatics model based on statistical and machine learning approaches for the accurate prediction of genotoxicity of different types of MWCNTs. Towards this goal, a number of different computational workflows were designed, combining unsupervised (Principal Component Analysis, PCA) and supervised classification techniques (Support Vectors Machine, “SVM”, Random Forest, “RF”, Logistic Regression, “LR” and Naïve Bayes, “NB”) and Bayesian optimization. The Recursive Feature Elimination (RFE) method was applied for selecting the most important variables. An RF model using only three features was selected as the most efficient for predicting the genotoxicity of MWCNTs, exhibiting 80% accuracy on external validation and high classification probabilities. The most informative features selected by the model were “Length”, “Zeta average” and “Purity”.

Pleural mesothelioma classification update.

The 2015 WHO classification of pleural mesotheliomas includes three major histologic subtypes-epithelioid, sarcomatoid, and biphasic. Recent genomic data has supported the need for a more granular and clinically valid classification beyond the three current subtypes. Because of tumor rarity and overlapping histologic features with other tumor types, diagnostic immunohistochemical work up is essential component in establishing the final diagnosis of mesothelioma. The use of BAP1 and CDKN2A/MTAP improves the diagnostic sensitivity of effusion specimens and are valuable in establishing the diagnosis of epithelioid mesothelioma. The major change in the forthcoming WHO classification is the inclusion of mesothelioma in situ as a diagnostic category. In this review, we discuss recently proposed changes in the histologic classification of pleural mesothelioma, differential diagnosis, and importance of ancillary diagnostic studies.

УМОВИ ФОРМУВАННЯ ПОВЕРХНЕВОГО СТОКУ В МЕЖАХ УРБАНІЗОВАНИХ ТЕРИТОРІЙ ПРИ ВИПАДАННІ ДУЖЕ СИЛЬНИХ ТА НЕБЕЗПЕЧНИХ ОПАДІВ

The research investigates atmospheric precipitation, which according to Ukrainian national regulation has reached the criteria of heavy (≥ 50 mm at ≤ 12 hours) and dangerous (15 – 49 mm at ≤ 12 hours). A total of 98 cases of heavy precipitation (2005 – 2018), and 14 cases of dangerous precipitation (2017 – 2018) were analyzed. The research focuses on the formation conditions and volumes of surface runoff that forms on various types of underlying surface. To obtain a statistically valid classification, a cluster analysis of heavy and dangerous precipitation was carried out. The analysis allowed to distinguish three blocks or clusters, one of which corresponds to the type “mix” of heavy and slight precipitations during the development of frontal stratus clouds with so-called “submerged or flooded” convection, the second cluster corresponds to the type “heavy precipitations”, the third cluster is similar to the first cluster, but is marked by a decrease in the intensity of processes. The degree of connection between the intensity of heavy precipitation and the height of the clouds top was investigated. As a result, it was found that there is an inverse relation between the values. The physical features of the processes of cloud and precipitation formation was considered. The exceptional role of convective clouds in individual frontal massifs and also in the form of “submerged or flooded convection” in the massifs of stratus clouds in the formation of significant volumes of rainwater on the underlying surface was noted. The structure of the temporal changes in precipitation intensity was established: the maximum intensity values, the time of their occurrence, the availability and number of amplification waves and their temporal parameters. On the basis of the obtained indicators, the quantity of the surface runoff for determined processes and for amplification periods were calculated. As a result, it was found that the surface runoff, which formed in the built-up areas, exceeds the surface runoff from the moderately built-up area with almost no artificial pavement by 100 – 300 %.

Cluster analysis of impairment measures to inform an evidence-based classification structure in RaceRunning, a new World Para Athletics event for athletes with hypertonia, ataxia or athetosis

ABSTRACT RaceRunning enables athletes with limited or no walking ability to propel themselves independently using a three-wheeled frame that has a saddle, handle bars and a chest plate. For RaceRunning to be included as a para athletics event, an evidence-based classification system is required. This study assessed the impact of trunk control and lower limb impairment measures on RaceRunning performance and evaluated whether cluster analysis of these impairment measures produces a valid classification structure for RaceRunning. The Trunk Control Measurement Scale (TCMS), Selective Control Assessment of the Lower Extremity (SCALE), the Australian Spasticity Assessment Scale (ASAS), and knee extension were recorded for 26 RaceRunning athletes. Thirteen male and 13 female athletes aged 24 (SD = 7) years participated. All impairment measures were significantly correlated with performance (rho = 0.55–0.74). Using ASAS, SCALE, TCMS and knee extension as cluster variables in a two-step cluster analysis resulted in two clusters of athletes. Race speed and the impairment measures were significantly different between the clusters (p < 0.001). The findings of this study provide evidence for the utility of the selected impairment measures in an evidence-based classification system for RaceRunning athletes.

Rationale for Replacement of the Destructive Test by Non-Destructive One in Medical Devices Manufacturing

Due to expanding demand for the level of testing on one side and reduction of costs on the other side, the question how to replace expensive destructive testing of medical devices without compromising the quality of final product arising urgently. This situation is common within all highly regulated industries – in this article is addressed the problem from medical device manufacturing industry. Based on real data containing testing and validation datasets, logit model and classification tree model are estimated for establishing the relationship between result of destructive test and measurements of explored device. Results point to possibility of replacing destructive test by non-destructive one in our case.

2015 World Health Organization Classification of Pulmonary Tumors “A Valid Classification Until the New Classification”

2015 World Health Organization Classification of Pulmonary Tumors “A Valid Classification Until the New Classification”

Investigating the utility of a kindergarten number line assessment compared to an early numeracy screening battery

Drawing from the developmental and cognitive mathematics literature, the purpose of this study was to investigate the reliability, validity, and diagnostic utility of a widely-researched number line task in kindergarten. Specifically, the Number Line Assessment 0−100 (NLA 0−100) as compared to an established kindergarten screening measure was examined using (a) regression models and (b) classification accuracy. Five- and six-year-old students (n = 154) were assessed on numeracy measures in the fall and spring of their kindergarten year. The NLA 0−100 had lower predictive validity and lower classification accuracy compared to an early numeracy screening measure. The findings have implications for practice and future research using number line screening assessments.

PENGEMBANGAN MODUL LARUTAN PENYANGGA BERBASIS PENDEKATAN TERPADU STEM

This study aims to determine the feasibility, student response and effectiveness of buffer solution modules based on the integrated STEM approach developed. This research is a development research adapted from the development model of Borg and Gall. The study was conducted in January - April 2019 at SMAN 09 Kota Bengkulu. The research data were obtained from the results of interviews, validation questionnaires, student response questionnaires and understanding tests. The selection of the trial class was carried out through a normality test for all class XI MIPA with a total subject of 111 students. The results of the trial were obtained by two respondent classes namely XI MIPA 1 for trial use and XI MIPA 2 for product testing with a total of 38 students. The results of the validation test obtained an average assessment of material experts 4.3 and 4.7 media experts who were both in the range of 4,2 ? &lt; 5 with a very valid classification. In product testing and usage tests, the average score of students' responses was 4.3 with a very interesting classification. The effectiveness of the module is seen from the understanding test with the percentage of student learning completeness of 86.21% reaching the KKM which is categorized as effective. So it can be concluded that, in general, the module based on the solution based on the STEM approach developed can be used in the learning process in class XI MIPA SMAN 09 Kota Bengkulu in the academic year 2018/2019.

The Robson classification for caesarean section-A proposed method based on routinely collected health data.

BackgroundWith an increasing rate of caesarean sections as well as rising numbers of multiple pregnancies, valid classifications for benchmarking are needed. The Robson classification provides a method to group cases with caesarean section in order to assess differences in outcome across regions and sites. In this study we set up a novel method of classification by using routinely collected health data. We hypothesize i that routinely collected health data can be used to apply complex medical classifications and ii that the Robson classification is capable of classifying mothers and their corresponding newborn into meaningful groups with regard to outcome.Methods and findingsThe study was conducted at the coding department and the department of obstetrics and gynecology Inselspital, University Hospital of Bern, Switzerland. The study population contained inpatient cases from 2014 until 2017. Administrative and health data were extracted from the Data Warehouse. Cases were classified by a Structured Query Language code according to the Robson criteria using data from the administrative system, the electronic health record and from the laboratory system. An automated query to classify the cases according to Robson could be implemented and successfully validated. A linkage of the mother’s class to the corresponding newborn could be established. The distribution of clinical indicators was described. It could be shown that the Robson classes are associated to outcome parameters and case related costs.ConclusionsWith this study it could be demonstrated, that a complex query on routinely collected health data would serve for medical classification and monitoring of quality and outcome. Risk-stratification might be conducted using this data set and should be the next step in order to evaluate the Robson criteria and outcome. This study will enhance the discussion to adopt an automated classification on routinely collected health data for quality assurance purposes.

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.