Soft tissue grafting to improve the attached mucosa at dental implants: A review of the literature and proposal of a decision tree.

BACKGROUND Scientific data and clinical observations appear to indicate that an adequate width of attached mucosa may facilitate oral hygiene procedures thus preventing peri-implant inflammation and tissue breakdown (eg, biologic complications). Consequently, in order to avoid biologic complications and improve long-term prognosis, soft tissue conditions should be carefully evaluated when implant therapy is planned. At present the necessity and time-point for soft tissue grafting (eg, prior to or during implant placement or after healing) is still controversially discussed while clinical recommendations are vague. OBJECTIVES To provide a review of the literature on the role of attached mucosa to maintain periimplant health, and to propose a decision tree which may help the clinician to select the appropriate surgical technique for increasing the width of attached mucosa. RESULTS The available data indicate that ideally, soft tissue conditions should be optimized by various grafting procedures either before or during implant placement or as part of stage-two surgery. In cases, where, despite insufficient peri-implant soft tissue condition (ie, lack of attached mucosa or movements caused by buccal frena), implants have been uncovered and/or loaded, or in cases where biologic complications are already present (eg, mucositis, peri-implantitis), the treatment appears to be more difficult and less predictable. CONCLUSION Soft tissue grafting may be important to prevent peri-implant tissue breakdown and should be considered when dental implants are placed. The presented decision tree may help the clinician to select the appropriate grafting technique.

Objective consensus from decision trees

BackgroundConsensus-based approaches provide an alternative to evidence-based decision making, especially in situations where high-level evidence is limited. Our aim was to demonstrate a novel source of information, objective consensus based on recommendations in decision tree format from multiple sources.MethodsBased on nine sample recommendations in decision tree format a representative analysis was performed. The most common (mode) recommendations for each eventuality (each permutation of parameters) were determined. The same procedure was applied to real clinical recommendations for primary radiotherapy for prostate cancer. Data was collected from 16 radiation oncology centres, converted into decision tree format and analyzed in order to determine the objective consensus.ResultsBased on information from multiple sources in decision tree format, treatment recommendations can be assessed for every parameter combination. An objective consensus can be determined by means of mode recommendations without compromise or confrontation among the parties. In the clinical example involving prostate cancer therapy, three parameters were used with two cut-off values each (Gleason score, PSA, T-stage) resulting in a total of 27 possible combinations per decision tree. Despite significant variations among the recommendations, a mode recommendation could be found for specific combinations of parameters.ConclusionRecommendations represented as decision trees can serve as a basis for objective consensus among multiple parties.

The challenge of triaging chest pain patients: the bernese university hospital experience

Accurate diagnosis of the causes of chest pain and dyspnea remain challenging. In this preliminary observational study with a 5-year follow-up, we attempted to find a simplified approach to selecting patients with chest pain needing immediate care based on the initial evaluation in ED. During a 24-month period were randomly selected 301 patients and a conditional inference tree (CIT) was used as the basis of the prognostic rule. Common diagnoses were musculoskeletal chest pain (27%), ACS (19%) and panic attack (12%). Using variables of ACS symptoms we estimated the likelihood of ACS based on a CIT to be high at 91% (32), low at 4% (198) and intermediate at 20.5-40% in (71) patients. Coronary catheterization was performed within 24 hours in 91% of the patients with ACS. A culprit lesion was found in 79%. Follow-up (median 4.2 years) information was available for 70% of the patients. Of the 164 patients without ACS who were followed up, 5 were treated with revascularization for stable angina pectoris, 2 were treated with revascularization for myocardial infarction, and 25 died. Although a simple triage decision tree could theoretically help to efficient select patients needing immediate care we need also to be vigilant for those presenting with atypical symptoms.

Spatial Assessment of Erosion and its Impact on Soil Fertility in the Tajik Foothills

Efficient planning of soil conservation measures requires, first, to understand the impact of soil erosion on soil fertility with regard to local land cover classes; and second, to identify hot spots of soil erosion and bright spots of soil conservation in a spatially explicit manner. Soil organic carbon (SOC) is an important indicator of soil fertility. The aim of this study was to conduct a spatial assessment of erosion and its impact on SOC for specific land cover classes. Input data consisted of extensive ground truth, a digital elevation model and Landsat 7 imagery from two different seasons. Soil spectral reflectance readings were taken from soil samples in the laboratory and calibrated with results of SOC chemical analysis using regression tree modelling. The resulting model statistics for soil degradation assessments are promising (R2=0.71, RMSEV=0.32). Since the area includes rugged terrain and small agricultural plots, the decision tree models allowed mapping of land cover classes, soil erosion incidence and SOC content classes at an acceptable level of accuracy for preliminary studies. The various datasets were linked in the hot-bright spot matrix, which was developed to combine soil erosion incidence information and SOC content levels (for uniform land cover classes) in a scatter plot. The quarters of the plot show different stages of degradation, from well conserved land to hot spots of soil degradation. The approach helps to gain a better understanding of the impact of soil erosion on soil fertility and to identify hot and bright spots in a spatially explicit manner. The results show distinctly lower SOC content levels on large parts of the test areas, where annual crop cultivation was dominant in the 1990s and where cultivation has now been abandoned. On the other hand, there are strong indications that afforestations and fruit orchards established in the 1980s have been successful in conserving soil resources.

Perspectives on periodontal risk factors

The development of a clinical decision tree based on knowledge about risks and reported outcomes of therapy is a necessity for successful planning and outcome of periodontal therapy. This requires a well-founded knowledge of the disease entity and a broad knowledge of how different risk conditions attribute to periodontitis. The infectious etiology, a complex immune response, and influence from a large number of co-factors are challenging conditions in clinical periodontal risk assessment. The difficult relationship between independent and dependent risk conditions paired with limited information on periodontitis prevalence adds to difficulties in periodontal risk assessment. The current information on periodontitis risk attributed to smoking habits, socio-economic conditions, general health and subjects' self-perception of health, is not comprehensive, and this contributes to limited success in periodontal risk assessment. New models for risk analysis have been advocated. Their utility for the estimation of periodontal risk assessment and prognosis should be tested. The present review addresses several of these issues associated with periodontal risk assessment.

Predictors of attempted suicide among Swiss patients with alcohol-use disorders

Objective: Suicide attempts are common in patients being treated for alcohol-use disorders (AUDs). However, clinical assessment of suicide risk is difficult. In this Swiss multisite study, we propose a decision tree to facilitate identification of profiles of AUD patients at high risk for suicidal behavior. Method: In this retrospective study, we used a sample of 700 patients (243 female), attending 1 of 12 treatment programs for AUDs in the German-speaking part of Switzerland. Sixty-nine patients who reported a suicide attempt in the 3 months before the index treatment were compared using risk factors with 631 patients without a suicide attempt. Receiver operating characteristic (ROC) analyses were used to identify patients at risk of having had a suicide attempt in the previous 3 months. Results: Consistent with previous empirical findings in AUD patients, a prior history of attempted suicide and severe symptoms of depression and aggression considerably increased the risk of a suicide attempt and, in combination, raised the likelihood of a prior suicide attempt to 52%. In addition, one third of AUD patients who had a history of suicide attempts and previous inpatient psychiatric treatment, or who were male and had previous inpatient psychiatric treatment, also reported a suicide attempt. Conclusions: The empirically supported decision tree helps to identify profiles of suicidal AUD patients in Switzerland and supplements clinicians' judgments in making triage decisions for suicide management.

Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

Influence of hormone replacement therapy on proteomic pattern in serum of postmenopausal women

OBJECTIVES: Proteomics approaches to cardiovascular biology and disease hold the promise of identifying specific proteins and peptides or modification thereof to assist in the identification of novel biomarkers. METHOD: By using surface-enhanced laser desorption and ionization time of flight mass spectroscopy (SELDI-TOF-MS) serum peptide and protein patterns were detected enabling to discriminate between postmenopausal women with and without hormone replacement therapy (HRT). RESULTS: Serum of 13 HRT and 27 control subjects was analyzed and 42 peptides and proteins could be tentatively identified based on their molecular weight and binding characteristics on the chip surface. By using decision tree-based Biomarker Patternstrade mark Software classification and regression analysis a discriminatory function was developed allowing to distinguish between HRT women and controls correctly and, thus, yielding a sensitivity of 100% and a specificity of 100%. The results show that peptide and protein patterns have the potential to deliver novel biomarkers as well as pinpointing targets for improved treatment. The biomarkers obtained represent a promising tool to discriminate between HRT users and non-users. CONCLUSION: According to a tentative identification of the markers by their molecular weight and binding characteristics, most of them appear to be part of the inflammation induced acute-phase response

A systematized workflow for assessing land resources using readily available spatial datasets

Land degradation as well as land conservation maps at a (sub-) national scale are critical for pro-ject planning for sustainable land management. It has long been recognized that online accessible and low-cost raster data sets (e.g. Landsat imagery, SRTM-DEM’s) provide a readily available basis for land resource assessments for developing countries. However, choice of spatial, tempo-ral and spectral resolution of such data is often limited. Furthermore, while local expert knowl-edge on land degradation processes is abundant, difficulties are often encountered when linking existing knowledge with modern approaches including GIS and RS. The aim of this study was to develop an easily applicable, standardized workflow for preliminary spatial assessments of land degradation and conservation, which also allows the integration of existing expert knowledge. The core of the developed method consists of a workflow for rule-based land resource assess-ment. In a systematic way, this workflow leads from predefined land degradation and conserva-tion classes to field indicators, to suitable spatial proxy data, and finally to a set of rules for clas-sification of spatial datasets. Pre-conditions are used to narrow the area of interest. Decision tree models are used for integrating the different rules. It can be concluded that the workflow presented assists experts from different disciplines in col-laboration GIS/RS specialists in establishing a preliminary model for assessing land degradation and conservation in a spatially explicit manner. The workflow provides support when linking field indicators and spatial datasets, and when determining field indicators for groundtruthing.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

Towards Automatic Identification of Discourse Markers in Dialogs: the Case of 'Like'

This article discusses the detection of discourse markers (DM) in dialog transcriptions, by human annotators and by automated means. After a theoretical discussion of the definition of DMs and their relevance to natural language processing, we focus on the role of like as a DM. Results from experiments with human annotators show that detection of DMs is a difficult but reliable task, which requires prosodic information from soundtracks. Then, several types of features are defined for automatic disambiguation of like: collocations, part-of-speech tags and duration-based features. Decision-tree learning shows that for like, nearly 70% precision can be reached, with near 100% recall, mainly using collocation filters. Similar results hold for well, with about 91% precision at 100% recall.