Cauda equina syndrome (CES) is a neurological syndrome presenting with non-specific symptoms and signs that often leads to diagnostic confusion and delay. Acute onset CES is a surgical emergency. The common aetiology is a prolapsed lumbar disc. If the diagnosis is missed, it can have devastating consequences for the patient and a high financial cost to healthcare providers. The objective of this study was to evaluate the efficacy of clinical assessment in clinching the diagnosis. Eighty patients who underwent urgent clinical assessment and magnetic resonance imaging (MRI) for suspected CES over a 1-year period (from January 1st 2008 to 31 December 2008) were included in the study. Fifteen of these patients had a CES and underwent urgent lumbar discectomy and decompression. Medical notes and MRI scans of all these patients were reviewed. The presenting symptoms and signs were analysed against a positive MRI scan. Chi-square test with Yates correction was used to test association of each clinical symptom and sign for a positive MRI. In this study, only 18.8% of assessed patients had a CES producing compression seen on the MRI. Presence of saddle sensory deficit was the only clinical feature with a statistically significant association with MRI positive CES (p = 0.03). This series shows that saddle sensory deficit has a higher predictive value than other clinical features in diagnosing a CES. However, as there is no symptom or sign which has an absolute predictive value in establishing the diagnosis of CES, any patient in whom a reasonable suspicion of CES arises must undergo urgent MRI to exclude this diagnosis.

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In this paper, we describe the validation of GNPy. GNPy is an open source application that approaches the optical layer according to a disaggregated paradigm, and its core engine is a quality-of-transmission estimator for coherent wavelength division multiplexed optical networks. This software is versatile. It can be used to prepare a request for proposal/request for quotation, as an engine of a what-if analysis on the physical layer, to optimize the network configuration to maximize the channel capacity, and to investigate the capacity and performance of a deployed network. We validate GNPy by feeding it with data from the network controller and comparing the results to experimental measurements on mixed-fiber, Raman-amplified, multivendor scenarios over the full C-band. We then test transmission distances from 400 up to 4000 km, polarization-multiplexed (PM) quadrature phase shift keying, the PM-8 quadrature amplitude modulation (QAM) and PM-16QAM formats, erbium-doped fiber amplifier (EDFA) and mixed Raman–EDFA amplification, and different power levels. We show excellent accuracy in predicting both the optical signal-to-noise ratio and the generalized signal-to-noise ratio (GSNR), within 1 dB accuracy for more than 90% of the 500 experimental samples. We also demonstrate the ability to estimate the transmitted power maximizing the GSNR within 0.5 dB of accuracy.

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