Canine central nervous system inflammatory diseases: a common quandary?
Bibliographic record
Abstract
EVEN though the field of companion animal neurology continues to advance almost synchronously with technological developments, we continue to face the same practical questions about the diagnosis and treatment of central nervous system (CNS) diseases which make us realise that the “dark-ages” are not far behind us! It seems that in this time of routine polymerase chain reaction (PCR) and MRI evaluations, we should never have to struggle with the specifics of a diagnosis or “guess” the most appropriate treatments. However, with respect to the CNS inflammatory diseases affecting dogs and cats, “struggle” and “guess” are what we do best. The literature is constantly improving our knowledge about the pathophysiology of infectious and “pathogen-free” inflammatory diseases affecting the nervous system in dogs, but practically we are often left unable to achieve a specific diagnosis on an individual case basis. When our therapeutic choices are antibiotics and/or immune suppression we face a potentially devastating dichotomy. As the paper in this issue by Espino and others (2006) highlights, inflammatory CNS disease can potentially be fatal. Inflammatory diseases are seemingly relatively common causes of CNS dysfunction, with their epidemiology being largely affected by geography. In this issue, Fluehmann and colleagues document that inflammatory diseases were the second most frequent cause of canine neurological dysfunction over an 11-year period, at their referral hospital in Switzerland (Fluehmann and others 2006). The absolute frequency of neurological cases which these represented, though, was only 14 per cent, so perhaps the concerns about specific diagnosis and appropriate treatments are over-stated? Just quite how common inflammatory diseases of the CNS are in our companion animals in each country is unknown. The diagnosis of inflammatory causes of CNS disease relies on combining the patient’s signalment, history, presenting signs and neurological examination with the results of blood work, infectious disease titres, cerebrospinal fluid (CSF) analysis (including culture and PCR analysis) and advanced imaging such as MR investigations. Analysing CSF may still be our best hope at confirming an inflammatory disease and suggesting a cause. In addition to its routine analysis, CSF culture, titres, electrophoresis and PCR analysis can be requested. However, at present its utility still leaves a lot to be desired. In the presence of bacterial meningoencephalitis, CSF culture is rarely positive (Radaelli and Platt 2002). Interpretation of CSF antibody titres when performed alone may not be accurate because intrathecal antibody production can be difficult to differentiate from a disruption of the blood brain barrier or transudation of serum antibodies due to blood contamination of the sample. High resolution electrophoresis of CSF may be helpful to differentiate dogs with inflammatory disease that produce mild blood brain barrier impairment from those that have intrathecal production of gamma globulins, but recent work has not confirmed this to be a valuable ancillary diagnostic tool (Behr and others 2006). High values of CSF IgA levels have been found in the majority of dogs with steroid responsive meningo-arteritis, but they can also be found in other inflammatory diseases (Tipold and others 1995). Polymerase chain reaction (PCR) has been used to detect several infectious agents in the CSF, especially Toxoplasma gondii, Neospora caninuum, and canine distemper virus in dogs (Stiles and others 1996, Frisk and others 1999, Schatzberg and others 2003, Saito and others 2006). Although a proven useful complementary test, PCR cannot be relied upon as being 100 per cent sensitive (Schatzberg and others 2003). The prevalence of auto-antibody in the CSF from dogs with various central nervous system diseases has been documented recently (Matsuki and others 2004); the prevalence of anti-astrocytic auto-antibody was highly specific to necrotising meningoencephalitis (NME) and granulomatous meningoencephalitis (GME) but they were also detected in dogs with brain tumours (Matsuki and others 2004). Recent publications describing the MR imaging appearance of inflammatory CNS diseases such as NME and GME have been helpful in highlighting how non-specific advanced imaging modalities can be and, worryingly, how insensitive they may be when compared to CSF analysis (Benigni and Lamb 2005, Cherubini and others 2005, Lamb and others 2005, Cherubini and others 2006, von Praun and others 2006). Tissue biopsy and culture may seem extreme when considering a lesion located in the CNS, but this may the only method of more accurately diagnosing these cases. Performing all of the aforementioned tests may not be practically or economically viable; however, even when utilising their results, we often find ourselves at the same end-point of an uncertain diagnosis! Over the last few years, there have been several publications suggesting newer immunosuppressive drug therapies for the group of “pathogen-free” CNS inflammatory diseases. Such diseases include GME, NME and necrotising leucoencephalitis. The treatments have included cyclosporine (Adamo and O’Brien 2004, Gnirs 2006), leflunomide (Gregory and others 1998), azathioprine (Ryan and others 2001) and cytosine arabinoside (Nuhsbaum and others 2002) all documented in relatively few cases. Although initial comparative data was presented at this year’s BSAVA Annual Congress (Penning and others 2006), there has been no published documentation of the risks and benefits of these treatments when compared to the use of immunosuppressive regimens of corticosteroids alone for the same disease. In this issue, Zarfoss and others (2006), evaluate the use of cytosine arabinoside in combination with prednisone therapy for dogs with CNS inflammatory disease of unknown aetiology. A suggested safe and beneficial therapy is concluded. The authors have made a worthy effort in detailing the concerns that practically surround the diagnosis and treatment of CNS inflammatory diseases, and so provide a platform for future publications to acknowledge that investigations of novel treatments need to be tinged with realism as well as the necessary scientific approach. Simon Platt qualified from the University of Edinburgh in 1992 at which time he undertook a one-year internship in small animal medicine and surgery at the University of Guelph in Canada. After spending two years in small animal practice in the UK, Simon began a three-year residency in neurology and neurosurgery at the University of Florida. In 1998, he was appointed as assistant professor of neurology/neurosurgery at the University of Georgia. Presently head of the neurology unit at the Animal Health Trust in Newmarket, Simon is a diplomate of the American College of Veterinary Internal Medicine in Neurology and of the European College of Veterinary Neurology (ECVN) as well as being a RCVS-recognised specialist in veterinary neurology. Simon is currently the chair of the examination committee for the ECVN and is co-editor of the new “BSAVA Manual of Canine and Feline Neurology”
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".