For example, if we know that areas B and C in Fig. 14.1B are 10 cm apart and that conduction velocities of the fibers between B and C are ∼1 m/s, we can expect delays ∼100 ms plus a few milliseconds for each synapse involved. Because we often know the typical conduction velocity and delays caused by synaptic transmission, we can (at least) recognize unrealistic delays. Often, authors use terms such as “functional connectivity” or “synaptic flow” to (implicitly) indicate the caveats above. Having said this, in many studies in neuroscience this is (conveniently) ignored and timing in signals is frequently used as an argument for connectivity. So equating lead-lag with causality/connectivity can be incorrect.
It would get even worse if we hadn't recorded from A in this example: then we only find B → C and we would be 100% incorrect. We are only partly correct: the two former relationships are correctly inferred but the latter is not. However, if we measure signals from A, B, and C ( Fig. 14.1B), we conclude that A → B, A → C, and B → C.
If we record from areas A and B in Fig. 14.1A, our method of interpreting lead-lag as a causal relationship A → B is correct.
CAUSALITY 7 FULL
The most frequent causality category observed by the WHO-UMC criteria, Naranjo as well as the Liverpool algorithm was "Probable." Full concurrence was not found between any of two scales of causality assessment.Īdverse drug reaction Naranjo's adverse drug reaction probability scale World Health Organization-Uppsala Monitoring Centre causality assessment system causality assessment kappa pharmacovigilance.We have to start pessimistically by pointing out that translation from lead-lag to causality is not strictly possible-the example in Fig. 14.1 demonstrates this. Negative and poor concurrence based on kappa values was seen between WHO and Liverpool causality comparison (κ = -0.161).
Positive but poor concurrence based on kappa values was seen between Liverpool and Naranjo's causality comparison (κ = 0.133). Cohen's kappa test shows that negative and poor concurrence was seen between WHO and Naranjo causality comparison (κ = -0.161). Causality assessment of adverse reactions according to Liverpool criteria shows that 61.9% cases were of probable type, 4.8% cases were possible and 33.3% cases were definite. Causality assessment of adverse reactions according to WHO-UMC criteria shows that 85.7% cases were of probable type, 4.8% cases were possible, 4.8% cases were unlikely and 4.8% cases were definite. Concurrence between the two algorithms was compared using the Cohen's weighted kappa statistic.Ĭausality assessment of adverse reactions according to Naranjo criteria shows that 81% cases were of probable type, 9.5% cases were possible and 9.5% cases were unlikely. Causality assessment was performed by two well-trained independent pharmacologists by applying the three methods-WHO, Naranjo and LCAT. All the ADRs which were reported by the Pharmacovigilance Unit between July 2016 and March 2017 were assessed. This was a cross-sectional retrospective study. The secondary objective was to assess the reported adverse drug reactions in a tertiary care hospital in South India. Other primary objective was to assess the agreement between the WHO-UMC criterion, Naranjo algorithm and LCAT. The primary objective of this study was to assess the causality of ADRs using World Health Organization-Uppsala Monitoring Centre (WHO-UMC), Naranjo and Liverpool ADR Causality Assessment Tool (LCAT).