Right secondary somatosensory cortex-a promising novel target for the treatment of drug-resistant neuropathic orofacial pain with repetitive transcranial magnetic stimulation.

High-frequency repetitive transcranial magnetic stimulation (rTMS) of the motor cortex has analgesic effect; however, the efficacy of other cortical targets and the mode of action remain unclear. We examined the effects of rTMS in neuropathic orofacial pain, and compared 2 cortical targets against placebo. Furthermore, as dopaminergic mechanisms modulate pain responses, we assessed the influence of the functional DRD2 gene polymorphism (957C>T) and the catechol-O-methyltransferase (COMT) Val158Met polymorphism on the analgesic effect of rTMS. Sixteen patients with chronic drug-resistant neuropathic orofacial pain participated in this randomized, placebo-controlled, crossover study. Navigated high-frequency rTMS was given to the sensorimotor (S1/M1) and the right secondary somatosensory (S2) cortices. All subjects were genotyped for the DRD2 957C>T and COMT Val158Met polymorphisms. Pain, mood, and quality of life were monitored throughout the study. The numerical rating scale pain scores were significantly lower after the S2 stimulation than after the S1/M1 (P = 0.0071) or the sham (P = 0.0187) stimulations. The Brief Pain Inventory scores were also lower 3 to 5 days after the S2 stimulation than those at pretreatment baseline (P = 0.0127 for the intensity of pain and P = 0.0074 for the interference of pain) or after the S1/M1 (P = 0.001 and P = 0.0001) and sham (P = 0.0491 and P = 0.0359) stimulations. No correlations were found between the genetic polymorphisms and the analgesic effect in the present small clinical sample. The right S2 cortex is a promising new target for the treatment of neuropathic orofacial pain with high-frequency rTMS.

Detection of outliers in reference distributions: performance of Horn's algorithm.

BACKGROUND: Medical laboratory reference data may be contaminated with outliers that should be eliminated before estimation of the reference interval. A statistical test for outliers has been proposed by Paul S. Horn and coworkers (Clin Chem 2001;47:2137-45). The algorithm operates in 2 steps: (a) mathematically transform the original data to approximate a gaussian distribution; and (b) establish detection limits (Tukey fences) based on the central part of the transformed distribution. METHODS: We studied the specificity of Horn's test algorithm (probability of false detection of outliers), using Monte Carlo computer simulations performed on 13 types of probability distributions covering a wide range of positive and negative skewness. Distributions with 3% of the original observations replaced by random outliers were used to also examine the sensitivity of the test (probability of detection of true outliers). Three data transformations were used: the Box and Cox function (used in the original Horn's test), the Manly exponential function, and the John and Draper modulus function. RESULTS: For many of the probability distributions, the specificity of Horn's algorithm was rather poor compared with the theoretical expectation. The cause for such poor performance was at least partially related to remaining nongaussian kurtosis (peakedness). The sensitivity showed great variation, dependent on both the type of underlying distribution and the location of the outliers (upper and/or lower tail). CONCLUSION: Although Horn's algorithm undoubtedly is an improvement compared with older methods for outlier detection, reliable statistical identification of outliers in reference data remains a challenge.

Partitioning biochemical reference data into subgroups: comparison of existing methods.

Four existing methods for partitioning biochemical reference data into subgroups are compared. Two of these, the method of Sinton et al. and that of Ichihara and Kawai, are based on a quotient of a difference between the subgroups and the reference interval for the combined distribution. The criterion of Sinton et al. appears rather stringent and could lead to recommendations to apply a common reference interval in many cases where establishment of group-specific reference intervals would be more useful. The method of Ichihara and Kawai is similar to that of Sinton et al., but their criterion, based on a quantity derived from between-group and within-group variances, seems to lead to inconsistent results when applied to some model cases. These two methods have the common weakness of using gross differences between subgroup distributions as an indicator of differences between their reference limits, while distributions with different means can actually have equal reference limits and those with equal means can have different reference limits. The idea of Harris and Boyd to require that the proportions of the subgroup distributions outside the common reference limits be kept reasonably close to the ideal value of 2.5% as a prerequisite for using common reference limits seems to have been a major improvement. The other two methods considered, that of Harris and Boyd and the "new method" follow this idea. The partitioning criteria of Harris and Boyd have previously been shown to provide a poor correlation to those proportions, however, and the weaknesses of their method are summarized in a list of five drawbacks. Different versions of the new method offer improvements to these drawbacks.

Proposal for guidelines to establish common biological reference intervals in large geographical areas for biochemical quantities measured frequently in serum and plasma.

A suggestion for a standard procedure to establish biological reference intervals for biochemical quantities by a multicenter approach is presented. This procedure was developed for and used in the Nordic Reference Interval Project 2000 (NORIP). This project established biological reference intervals for 25 frequently requested biochemical quantities through cooperation of 102 Nordic laboratories. Each laboratory performed collection of reference samples and measurement using their routine methods. The bias of each routine method was eliminated by use of common reference materials measured in each of the participating laboratories.

Partitioning of nongaussian-distributed biochemical reference data into subgroups.

BACKGROUND: The aim of this study was to develop new methods for partitioning biochemical reference data, covering in particular nongaussian distributions. METHODS: We recently proposed partitioning criteria for gaussian distributions. These criteria relate to proportions of the subgroups outside each of the reference limits of the combined distribution (proportion criteria) and to distances between the subgroup distributions as correlates of these proportions (distance criteria). However, distance criteria do not seem to be ideal for nongaussian distributions because a generally valid relationship between proportions and distances cannot be established for these. RESULTS: Proportion criteria appear preferable to distance criteria for two additional reasons: (a) The prevalences of the subgroup populations may have a considerable effect on stratification, but these are hard to account for by using distance criteria. Two methods to handle prevalences are described, the root method and the multiplication method. (b) Tied reference values, another complication of the partitioning problem, could also be hard to take care of using distance criteria. Some solutions to the problems caused by tied reference values are suggested. CONCLUSIONS: Partitioning of biochemical reference data should preferably be based on proportion criteria; this is particularly true for nongaussian distributions. Both of the described complications of the partitioning problem, the prevalences of the subgroups and tied reference values, are hard to deal with using distance criteria, but the proposed methods make it possible to account for them when proportion criteria are applied.

Impact of subgroup prevalences on partitioning of Gaussian-distributed reference values.

BACKGROUND: The aims of this report were to examine how unequal subgroup prevalences in the source population may affect reference interval partitioning decisions and to develop generally applicable guidelines for partitioning gaussian-distributed data. METHODS: We recently proposed a new model for partitioning reference intervals when the underlying data distribution is gaussian. This model is based on controlling the proportions of the subgroup distributions that fall outside each of the common reference limits, using the distances between the reference limits of the subgroup distributions as functions to these proportions. We examine the significance of the unequal prevalence effect for the partitioning problem and quantify it for distance partitioning criteria by deriving analytical expressions to express these criteria as a function of the ratio of prevalences. An application example, illustrating various aspects of the importance of the prevalence effect, is also presented. RESULTS: Dramatic shrinkage of the critical distances between reference limits of the subgroups needed for partitioning was observed as the ratio of prevalences, the larger one divided by the smaller one, was increased from unity. Because of this shrinkage, the same critical distances are not valid for all ratios of prevalences, but specific critical distances should be used for each particular value of this ratio. Although proportion criteria used in determining the need for reference interval partitioning are not dependent on the prevalence effect, this effect should be accounted for when these criteria are being applied by adjusting the sample sizes of the subgroups to make them correspond to the ratio of prevalences. CONCLUSIONS: The prevalences of subgroups in the reference population should be known and observed in the calculations for every reference interval study, irrespective of whether distance or proportion criteria are being used to determine the need for reference interval partitioning. We present detailed methods to account for the prevalences when applying each of these types of criteria. Analytical expressions for the distance criteria, to be used when high precision is needed, and approximate distances, to be used in practical work, are derived. General guidelines for partitioning gaussian distributed data are presented. Following these guidelines and using the new model, we suggest that partitioning can be performed more reliably than with any of the earlier models because the new model not only offers an improved correspondence between the critical distances and the critical proportions, but also accounts for the prevalence effect.

Objective criteria for partitioning Gaussian-distributed reference values into subgroups.

BACKGROUND: The aim of this study was to develop new and useful criteria for partitioning reference values into subgroups applicable to gaussian distributions and to distributions that can be transformed to gaussian distributions. METHODS: The proposed criteria relate to percentages of the subgroups outside each of the reference limits of the combined distribution. Critical values suggested as partitioning criteria for these percentages were derived from analytical bias quality specifications for using common reference intervals throughout a geographic area. As alternative partitioning criteria to the actual percentages, these were transformed mathematically to critical distances between the reference limits of the subgroup distributions, to be applied to each pair of reference limits, the upper and the lower, at a time. The new criteria were tested using data on various plasma proteins collected from approximately 500 reference individuals, and the outcomes were compared with those given by the currently widely applied and recommended partitioning model of Harris and Boyd, the "Harris-Boyd model". RESULTS: We suggest 4.1% as the critical minimum percentage outside that would justify partitioning into subgroups, and 3.2% as the critical maximum percentage outside that would justify combining them. Percentages between these two values should be classified as marginal, implying that nonstatistical considerations are required to make the final decision on partitioning. The correlation between the critical percentages and the critical distances was mathematically precise in the new model, whereas this correlation is rather approximate in the Harris-Boyd model because focus on the difference between means in this model makes high precision hard to achieve. The application examples suggested that the new model is more radical than the Harris-Boyd model. CONCLUSIONS: New percentage and distance criteria, to be used for partitioning gaussian-distributed data, have been developed. The distance criteria, applied separately to both reference limit pairs of the subgroup distributions, seemed more reliable and correlated more accurately with the critical percentages than the distance criteria of the Harris-Boyd model. As opposed to the Harris-Boyd model, the new model is easily adjustable to new critical values of the percentages, should they need to be changed in the future.