<b>Introduction</b>: Kinesiology-style manual muscle testing (kMMT) is a non-invasive assessment method used by various types of practitioners to detect a wide range of target conditions. It is distinctly different from the muscle testing performed in orthopaedic/neurological settings and from Applied kinesiology. Despite being estimated to be used by over 1 million people worldwide, the usefulness of kMMT has not yet been established. The aim of this thesis was to assess the validity of kMMT by examining its accuracy and precision. <b>Methods</b>: A series of 5 diagnostic test accuracy studies were undertaken. In the first study, the index test was kMMT, and the target condition was deceit in verbal statements spoken by Test Patients (TPs). The comparator reference standard was a true gold standard: the actual verity of the spoken statement. The outcomes of the muscle tests were interpreted consistently: a weak result indicated a Lie and a strong result indicated a Truth. A secondary index test was included as a comparator: Intuition, where Practitioners used intuition (without using kMMT) to ascertain if a Lie or Truth was spoken. Forty-eight Practitioners were recruited and paired with 48 unique kMMT-naïve TPs. Each Pair performed 60 kMMTs broken up into 6 blocks of 10, which alternated with blocks of 10 Intuitions. For each Pair, an overall percent correct was calculated for both kMMT and Intuition, and their means were compared. Also calculated for both tests were sensitivity, specificity, positive predictive value and negative predictive value. The second study was a replication of the first, using a sample size of 20 Pairs and a less complex procedure. In the third study, grip strength dynamometry replaced kMMT as the primary index test. In the fourth study, the reproducibility and repeatability of kMMT were examined. In the final study, TPs were presented with emotionally-arousing stimuli in addition to the affect-neutral stimuli used in previous studies, to assess if stimuli valence impacted kMMT accuracy. <b>Results</b>: Throughout this series of studies, mean kMMT accuracies (95% Confidence Intervals; CIs) ranged from 0.594 (0.541 – 0.647) to 0.659 (0.623 - 0.695) and mean Intuition accuracies, from 0.481 (0.456 - 0.506) to 0.526 (0.488 - 0.564). In all studies, mean kMMT accuracies were found to be significantly different from mean Intuition accuracies (p ≤ 0.01), and from Chance (p < 0.01). On the other hand, no difference was found between grip strength following False statements compared to grip strength following True statements (p = 0.61). In addition, the Practitioner-TP complex accounted for 57% of the variation in kMMT accuracy, with 43% unaccounted for. Also, there was no difference in the mean kMMT accuracy when using emotionally-arousing stimuli compared to when using affect-neutral stimuli (p = 0.35). Mean sensitivities (95% CI) ranged from 0.503 (0.421 - 0.584) to 0.659 (0.612 - 0.706) while mean specificities (95% CI) ranged from 0.638 (0.430 - 0.486) to 0.685 (0.616 - 0.754). Finally, while a number of participant characteristic seemed to influence kMMT accuracy during one study or another, no one specific characteristic was found to influence kMMT accuracy consistently (i.e. across the series of studies). <b>Discussion</b>: This series of studies has shown that kMMT can be investigated using rigorous evidence-based health care methods. Furthermore, for distinguishing lies from truths, kMMT has repeatedly been found to be significantly more accurate than both Intuition and Chance. Practitioners appear to be an integral part of the kMMT dynamic because when replaced by a mechanical device (i.e. a grip strength dynamometer), distinguishing Lies from Truth was not possible. In addition, since specificities seemed to be greater than sensitivities, Truths may have been easier to detect than Lies. A limitation of this series of studies is that I have a potential conflict of interest, in that I am a practitioner of kMMT who gets paid to perform kMMT. Another limitation is these results are not generalisable to other applications of kMMT, such as its use in other paradigms or using muscles other than the deltoid. Also, these results suggest that kMMT may be about 60&percnt; accurate, which is statistically different from Intuition and Chance; however it has not been established if 60&percnt; correct is "good enough" in a clinical context. As such, further research is needed to assess its clinical utility, such as randomised controlled trials investigating the effectiveness of whole kMMT technique systems. Also, future investigators may want to explore what factors, such as specific Practitioner and TP characteristics, influence kMMT accuracy, and to investigate the validity of using kMMT to detect other target conditions, using other reference standards and muscles other than the deltoid. <b>Summary</b>: This series of diagnostic test accuracy studies has found that kMMT can be investigated using rigorous methods, and that kMMT used to distinguish Lies from Truths is significantly more accurate that both Intuition and Chance. Further research is needed to assess kMMT’s clinical utility.

The collection of crop yield data has become much easier with the introduction of technologies such as the Global Positioning System (GPS), ground-based yield sensors, and Geographic Information Systems (GIS). This explosive growth and widespread use of spatial data has challenged the ability to derive useful spatial knowledge. In addition, outlier detection as one important pre-processing step remains a challenge because the technique and the definition of spatial neighbourhood remain non-trivial, and the quantitative assessments of false positives, false negatives, and the concept of region outlier remain unexplored. The overall aim of this study is to evaluate different spatial outlier detection techniques in terms of their accuracy and computational efficiency, and examine the performance of these outlier removal techniques in a site-specific management context. In a simulation study, unconditional sequential Gaussian simulation is performed to generate crop yield as the response variable along with two explanatory variables. Point and region spatial outliers are added to the simulated datasets by randomly selecting observations and adding or subtracting a Gaussian error term. With simulated data which contains known spatial outliers in advance, the assessment of spatial outlier techniques can be conducted as a binary classification exercise, treating each spatial outlier detection technique as a classifier. Algorithm performance is evaluated with the area and partial area under the ROC curve up to different true positive and false positive rates. Outlier effects in on-farm research are assessed in terms of the influence of each spatial outlier technique on coefficient estimates from a spatial regression model that accounts for autocorrelation. Results indicate that for point outliers, spatial outlier techniques that account for spatial autocorrelation tend to be better than standard spatial outlier techniques in terms of higher sensitivity, lower false positive detection rate, and consistency in performance. They are also more resistant to changes in the neighbourhood definition. In terms of region outliers, standard techniques tend to be better than spatial autocorrelation techniques in all performance aspects because they are less affected by masking and swamping effects. In particular, one spatial autocorrelation technique, Averaged Difference, is superior to all other techniques in terms of both point and region outlier scenario because of its ability to incorporate spatial autocorrelation while at the same time, revealing the variation between nearest neighbours. In terms of decision-making, all algorithms led to slightly different coefficient estimates, and therefore, may result in distinct decisions for site-specific management. The results outlined here will allow an improved removal of crop yield data points that are potentially problematic. What has been determined here is the recommendation of using Averaged Difference algorithm for cleaning spatial outliers in yield dataset. Identifying the optimal nearest neighbour parameter for the neighbourhood aggregation function is still non-trivial. The recommendation is to specify a large number of nearest neighbours, large enough to capture the region size. Lastly, the unbiased coefficient estimates obtained with Average Difference suggest it is the better method for pre-processing spatial outliers in crop yield data, which underlines its suitability for detecting spatial outlier in the context of on-farm research.

The collection of crop yield data has become much easier with the introduction of technologies such as the Global Positioning System (GPS), ground-based yield sensors, and Geographic Information Systems (GIS). This explosive growth and widespread use of spatial data has challenged the ability to derive useful spatial knowledge. In addition, outlier detection as one important pre-processing step remains a challenge because the technique and the definition of spatial neighbourhood remain non-trivial, and the quantitative assessments of false positives, false negatives, and the concept of region outlier remain unexplored. The overall aim of this study is to evaluate different spatial outlier detection techniques in terms of their accuracy and computational efficiency, and examine the performance of these outlier removal techniques in a site-specific management context. In a simulation study, unconditional sequential Gaussian simulation is performed to generate crop yield as the response variable along with two explanatory variables. Point and region spatial outliers are added to the simulated datasets by randomly selecting observations and adding or subtracting a Gaussian error term. With simulated data which contains known spatial outliers in advance, the assessment of spatial outlier techniques can be conducted as a binary classification exercise, treating each spatial outlier detection technique as a classifier. Algorithm performance is evaluated with the area and partial area under the ROC curve up to different true positive and false positive rates. Outlier effects in on-farm research are assessed in terms of the influence of each spatial outlier technique on coefficient estimates from a spatial regression model that accounts for autocorrelation. Results indicate that for point outliers, spatial outlier techniques that account for spatial autocorrelation tend to be better than standard spatial outlier techniques in terms of higher sensitivity, lower false positive detection rate, and consistency in performance. They are also more resistant to changes in the neighbourhood definition. In terms of region outliers, standard techniques tend to be better than spatial autocorrelation techniques in all performance aspects because they are less affected by masking and swamping effects. In particular, one spatial autocorrelation technique, Averaged Difference, is superior to all other techniques in terms of both point and region outlier scenario because of its ability to incorporate spatial autocorrelation while at the same time, revealing the variation between nearest neighbours. In terms of decision-making, all algorithms led to slightly different coefficient estimates, and therefore, may result in distinct decisions for site-specific management. The results outlined here will allow an improved removal of crop yield data points that are potentially problematic. What has been determined here is the recommendation of using Averaged Difference algorithm for cleaning spatial outliers in yield dataset. Identifying the optimal nearest neighbour parameter for the neighbourhood aggregation function is still non-trivial. The recommendation is to specify a large number of nearest neighbours, large enough to capture the region size. Lastly, the unbiased coefficient estimates obtained with Average Difference suggest it is the better method for pre-processing spatial outliers in crop yield data, which underlines its suitability for detecting spatial outlier in the context of on-farm research.

Predictive models based in symptomatology of suspected dengue patients seeking medical care in Honduras. The models based on logistic regression models predicted the outcomes of dengue fever/ severe dengue. Sensitivity and specificity are discussed. It also describe the level of agreement between Honduran classification of severe dengue and the ones based on World Health Organization guidelines of 1997 and 2009. / Introduction: Dengue is a major public health problem in tropical and subtropical countries but its clinical presentation may be similar to many febrile illnesses. Since in endemic countries laboratory confirmation is frequently delayed, the majority of dengue cases are diagnosed based on patient’s symptomatology. This can often lead to misdiagnosis and potential serious health complications. The objective of this study was to identify clinical, hematological and demographical parameters that could be used as predictors of dengue fever among patients with febrile illness. Methods: We conducted a retrospective cohort study of 548 patients presenting with febrile syndrome to the largest public hospitals in Honduras. Patients’ clinical, laboratory, and demographical data as well as dengue laboratory confirmation by either serology or viral isolation were used to build a predictive statistical model to identify dengue cases. Results: Of 548 patients, 390 were confirmed with dengue infection while 158 had negative results. Univariable analysis revealed seven variables associated with dengue: male sex, petechiae, skin rash, myalgia, retro-ocular pain, positive tourniquet test, and bleeding gums. In multivariable logistic regression analysis, retro-ocular pain petechiae and bleeding gums were associated with increased risk, while epistaxis and paleness of skin were associated with reduced risk of dengue. Using a value of 0.6 (i.e., 60% probability for a case to be positive based on the equation values), our model had a sensitivity of 86.2%, a specificity of 27.2%, and an overall accuracy of 69.2%; allowing for the diagnosis of dengue to be ruled out and for other febrile conditions to be investigated. Conclusions: The application of predictive models can be valuable when laboratory confirmation is delayed. Among Honduran patients presenting with febrile illness, our data reveal key symptoms associated with dengue fever, however the overall accuracy of our model is still low and specificity remains a concern. Our model requires validation in other populations with similar pattern of dengue transmission. Key Words: Dengue, fever, Predictive model, symptoms, Honduras / Thesis / Doctor of Philosophy (PhD) / Predictive models based in symptomatology of suspected dengue patients seeking medical care in Honduras. The models based on logistic regression models predicted the outcomes of dengue fever/ severe dengue. Sensitivity and specificity are discussed. It also describe the level of agreement between Honduran classification of severe dengue and the ones based on World Health Organization guidelines of 1997 and 2009.

A real-time polymerase chain reaction (qPCR) is considered the gold standard for the laboratory diagnosis of canine parvovirus (CPV) infection but can only be performed in specialized laboratories. Several point-of-care tests (POCT), detecting CPV antigens in faeces within minutes, are commercially available. The aim of this study was to evaluate eight POCT in comparison with qPCR. Faecal samples of 150 dogs from three groups (H: 50 client-owned, healthy dogs, not vaccinated within the last four weeks; S: 50 shelter dogs, healthy, not vaccinated within the last four weeks; p = 50 dogs with clinical signs of CPV infection) were tested with eight POCT and qPCR. Practicability, sensitivity, specificity, positive (PPV) and negative predictive values (NPV), as well as overall accuracy were determined. To assess the differences between and agreement among POCT, McNemar’s test and Cohen’s Kappa statistic were performed. Specificity and PPV were 100.0% in all POCT. Sensitivity varied from 22.9–34.3% overall and from 32.7–49.0% in group P. VetexpertRapidTestCPVAg® had the highest sensitivity (34.3% overall, 49.0% group P) and differed significantly from the 3 POCT with the lowest sensitivities (Fassisi®Parvo (27.7% overall, 36.7% group P), Primagnost®ParvoH+K (24.3% overall, 34.7% group P), FASTest®PARVOCard (22.9% overall, 32.7% group P)). The agreement among all POCT was at least substantial (kappa >0.80). A positive POCT result confirmed the infection with CPV in unvaccinated dogs, whereas a negative POCT result did not definitely exclude CPV infection due to the low sensitivity of all POCT.

Background: Parametrial tumor involvement is an important prognostic factor in cervical cancer and is used to guide management. Here, we investigate the diagnostic value of clinical examination under general anesthesia (EUA) and magnetic resonance imaging (MRI) in determining parametrial tumor spread. Methods: Post-operative pathological findings of 400 patients with primary cervical cancer were compared to the respective MRI data and the results from EUA. The gynecological oncologist had access to the MR images during clinical assessment (augmented EUA, aEUA). Results: Pathologically proven parametrial tumor invasion was present in 165 (41%) patients. aEUA exhibited a higher accuracy than MRI alone (83% vs. 76%; McNemar’s odds ratio [OR] = 2.0, 95%CI 1.25–3.27, p = 0.003). Although accuracy was not affected by tumor size in aEUA, MRI was associated with a lower accuracy in tumors ≥2.5 cm (OR for a correct diagnosis compared to smaller tumors 0.22, p < 0.001). There was also a decrease in specificity when evaluating parametrial invasion by MRI in tumors ≥2.5 cm in diameter (p < 0.0001) compared to smaller tumors (< 2.5 cm). Body mass index had no influence on performance of either method. Conclusions: aEUA has the potential to increase the diagnostic accuracy of MRI in determining parametrial tumor involvement in cervical cancer patients.

(1) Background: Antibody testing is commonly used to assess a dog’s immune status. For detection of antibodies against canine adenoviruses (CAVs), one point-of-care (POC) test is available. This study assessed the POC test´s performance. (2) Methods: Sera of 198 privately owned dogs and 40 specific pathogen-free (SPF) dogs were included. The reference standard for detection of anti-CAV antibodies was virus neutralization (VN) using CAV-1 and CAV-2 antigens. Specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy (OA) of the POC test were assessed. Specificity was considered most important. (3) Results: Prevalence of CAV-1 neutralizing antibodies (≥10) was 76% (182/238) in all dogs, 92% (182/198) in the subgroup of privately owned dogs, and 0% (0/40) in SPF dogs. Prevalence of CAV-2 neutralizing antibodies (≥10) was 76% (181/238) in all dogs, 91% (181/198) in privately owned dogs, and 0% (0/40) in SPF dogs. Specificity for detection of CAV-1 antibodies was lower (overall dogs, 88%; privately owned dogs, 56%; SPF dogs, 100%) compared with specificity for detection of CAV-2 antibodies (overall dogs, 90%; privately owned dogs, 65%; SPF dogs, 100%). (4) Conclusions: Since false positive results will lead to potentially unprotected dogs not being vaccinated, specificity should be improved to reliably detect anti-CAV antibodies that prevent infectious canine hepatitis in dogs.

Measuring antibodies to evaluate dogs’ immunity against canine parvovirus (CPV) is useful to avoid unnecessary re-vaccinations. The study aimed to evaluate the quality and practicability of four point-of-care (POC) tests for detection of anti-CPV antibodies. The sera of 198 client-owned and 43 specific pathogen-free (SPF) dogs were included; virus neutralization was the reference method. Specificity, sensitivity, positive and negative predictive value (PPV and NPV), and overall accuracy (OA) were calculated. Specificity was considered to be the most important indicator for POC test performance. Differences between specificity and sensitivity of POC tests in the sera of all dogs were determined by McNemar, agreement by Cohen’s kappa. Prevalence of anti-CPV antibodies in all dogs was 80% (192/241); in the subgroup of client-owned dogs, it was 97% (192/198); and in the subgroup of SPF dogs, it was 0% (0/43). FASTest® and CanTiCheck® were easiest to perform. Specificity was highest in the CanTiCheck® (overall dogs, 98%; client-owned dogs, 83%; SPF dogs, 100%) and the TiterCHEK® (overall dogs, 96%; client-owned dogs, 67%; SPF dogs, 100%); no significant differences in specificity were observed between the ImmunoComb®, the TiterCHEK®, and the CanTiCheck®. Sensitivity was highest in the FASTest® (overall dogs, 95%; client-owned dogs, 95%) and the CanTiCheck® (overall dogs, 80%; client-owned dogs, 80%); sensitivity of the FASTest® was significantly higher compared to the one of the other three tests (McNemars p-value in each comparison: <0.001). CanTiCheck® would be the POC test of choice when considering specificity and practicability. However, differences in the number of false positive results between CanTiCheck®, TiterCHEK®, and ImmunoComb® were minimal.

No description available.

Thesis (Ph.D.); Submitted to Iowa State Univ., Ames, IA (US); 12 Dec 2003. / Published through the Information Bridge: DOE Scientific and Technical Information. "IS-T 2028" Haibo Cao. 12/12/2003. Report is also available in paper and microfiche from NTIS.