Dental CT imaging as a screening tool for dental profiling: advantages and limitations

The use of dental processing software for computed tomography (CT) data (Dentascan) is described on postmortem (pm) CT data for the purpose of pm identification. The software allows reconstructing reformatted images comparable to conventional panoramic dental radiographs by defining a curved reconstruction line along the teeth on oblique images. Three corpses that have been scanned within the virtopsy project were used to test the software for the purpose of dental identification. In every case, dental panoramic images could be reconstructed and compared to antemortem radiographs. The images showed the basic component of teeth (enamel, dentin, and pulp), the anatomic structure of the alveolar bone, missing or unerupted teeth as well as restorations of the teeth that could be used for identification. When streak artifacts due to metal-containing dental work reduced image quality, it was still necessary to perform pm conventional radiographs for comparison of the detailed shape of the restoration. Dental identification or a dental profiling seems to become possible in a noninvasive manner using the Dentascan software.

Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker

A marker that is strongly associated with outcome (or disease) is often assumed to be effective for classifying individuals according to their current or future outcome. However, for this to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiological studies. An illustration of the relationship between odds ratios and receiver operating characteristic (ROC) curves shows, for example, that a marker with an odds ratio as high as 3 is in fact a very poor classification tool. If a marker identifies 10 percent of controls as positive (false positives) and has an odds ratio of 3, then it will only correctly identify 25 percent of cases as positive (true positives). Moreover, the authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker’s ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. The serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.

Use of multislice computed tomography in disaster victim identification--advantages and limitations

After a mass fatality incident (MFI), all victims have to be rapidly and accurately identified for juridical reasons as well as for the relatives' sake. Since MFIs are often international in scope, Interpol has proposed standard disaster victim identification (DVI) procedures, which have been widely adopted by authorities and forensic experts. This study investigates how postmortem multislice computed tomography (MSCT) can contribute to the DVI process as proposed by Interpol. The Interpol postmortem (PM) form has been analyzed, and a number of items in sections D and E thereof have been postulated to be suitable for documentation by CT data. CT scans have then been performed on forensic cases. Interpretation of the reconstructed images showed that indeed much of the postmortem information required for identification can be gathered from CT data. Further advantages of the proposed approach concern the observer independent documentation, the possibility to reconstruct a variety of images a long time after the event, the possibility to distribute the work by transmitting CT data digitally, and the reduction of time and specialists needed at the disaster site. We conclude that MSCT may be used as a valuable screening tool in DVI in the future.

Limitations of Remotely-sensed Aerosol as a Spatial Proxy for Fine Particulate Matter

Recent research highlights the promise of remotely-sensed aerosol optical depth (AOD) as a proxy for ground-level PM2.5. Particular interest lies in the information on spatial heterogeneity potentially provided by AOD, with important application to estimating and monitoring pollution exposure for public health purposes. Given the temporal and spatio-temporal correlations reported between AOD and PM2.5 , it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM2.5 . Here we find only limited spatial associations of AOD from three satellite retrievals with PM2.5 over the eastern U.S. at the daily and yearly levels in 2004. We then use statistical modeling to show that the patterns in monthly average AOD poorly reflect patterns in PM2.5 because of systematic, spatially-correlated error in AOD as a proxy for PM2.5 . Furthermore, when we include AOD as a predictor of monthly PM2.5 in a statistical prediction model, AOD provides little additional information to improve predictions of PM2.5 when included in a model that already accounts for land use, emission sources, meteorology and regional variability. These results suggest caution in using spatial variation in AOD to stand in for spatial variation in ground-level PM2.5 in epidemiological analyses and indicate that when PM2.5 monitoring is available, careful statistical modeling outperforms the use of AOD.

Meta-analysis in medical research: potentials and limitations

Meta-analysis, the statistical combination of results from several studies to produce a single estimate of a treatment effect or size of an association, continues to attract controversy. We illustrate and discuss the promises and limitations of meta-analysis. Meta-analysis of clinical trials can prevent delays in the introduction of effective treatments or lead to the timely identification of adverse effects. However, meta-analyses are liable to numerous biases, both at the level of the individual study and the selection of studies for inclusion in meta-analysis. The biases and confounding factors that threaten the validity of individual studies will also affect meta-analyses of observational studies. We argue that meta-analyses should only be performed within the framework of systematic reviews that have been prepared using methods that minimize bias and address the combinability of studies.