Reporting of conflicts of interest in guidelines of preventive and therapeutic interventions

BACKGROUND: Guidelines published in major medical journals are very influential in determining clinical practice. It would be essential to evaluate whether conflicts of interests are disclosed in these publications. We evaluated the reporting of conflicts of interest and the factors that may affect such disclosure in a sample of 191 guidelines on therapeutic and/or preventive measures published in 6 major clinical journals (Annals of Internal Medicine, BMJ, JAMA, Lancet, New England Journal of Medicine, Pediatrics) in 1979, 1984, 1989, 1994 and 1999. RESULTS: Only 7 guidelines (3.7%) mentioned conflicts of interest and all were published in 1999 (17.5% (7/40) of guidelines published in 1999 alone). Reporting of conflicts of interest differed significantly by journal (p=0.026), availability of disclosure policy by the journal (p=0.043), source of funding (p < 0.001) and number of authors (p=0.004). In the entire database of 191 guidelines, a mere 18 authors disclosed a total of 24 potential conflicts of interest and most pertained to minor issues. CONCLUSIONS: Despite some recent improvement, reporting of conflicts of interest in clinical guidelines published in influential journals is largely neglected

Detecting Complexity Abnormalities in Dyslexia Measuring Approximate Entropy of Electroencephalographic Signals

Dyslexia constitutes a specific reading disability, a condition characterized by severe difficulty in the mastery of reading despite normal intelligence or adequate education. Electroencephalogram (EEG) signal may be able to play an important role in the diagnosis of dyslexia. The Approximate Entropy (ApEn) is a recently formulated statistical parameter used to quantify the regularity of a time series data of physiological signals. In this paper, we initially estimated the ApEn values in signals recorded from controls subjects and dyslectic children. These values were firstly used for the statistical analysis of the two groups and secondly as feature input in a classification scheme. We also used the cross-ApEn methodology to get a measure of the asynchrony of the signals recorded from different electrodes. This preliminary study provides promising results towards correct identification of dyslexic cases, analyzing the corresponding EEG signals