Revealing clinically relevant specific IgE sensitization patterns in Hymenoptera venom allergy with dimension reduction and clustering.

Background: Immunoglobulin E (IgE) blood tests are used to detect sensitizations and potential allergies. Recent studies suggest that specific IgE sensitization patterns due to molecular interactions affect an individual's risk of developing allergic symptoms. Objective: The aim of this study was to reveal specific IgE sensitization patterns and investigate their clinical implications in Hymenoptera venom allergy. Methods: In this cross-sectional study, 257 hunters or fishers with self-filled surveys on previous Hymenoptera stings were analyzed. Blood samples were taken to determine Hymenoptera IgE sensitization levels. Using dimensionality reduction and clustering, specific IgE for 10 Hymenoptera venom allergens were evaluated for clinical relevance. Results: Three clusters were unmasked using novel dimensionality reduction and clustering methods solely based on specific IgE levels to Hymenoptera venom allergens. These clusters show different characteristics regarding previous systemic reactions to Hymenoptera stings. Conclusion: Our study was able to unmask non-linear sensitization patterns for specific IgE tests in Hymenoptera venom allergy. We were able to derive risk clusters for anaphylactic reactions following hymenoptera stings and pinpoint relevant allergens (rApi m 10, rVes v 1, whole bee, and wasp venom) for clustering.

Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study.

BACKGROUND: As advancements in artificial intelligence (AI) continue, large language models (LLMs) have emerged as promising tools for generating medical information. Their rapid adaptation and potential benefits in health care require rigorous assessment in terms of the quality, accuracy, and safety of the generated information across diverse medical specialties. OBJECTIVE: This study aimed to evaluate the performance of 4 prominent LLMs, namely, Claude-instant-v1.0, GPT-3.5-Turbo, Command-xlarge-nightly, and Bloomz, in generating medical content spanning the clinical specialties of ophthalmology, orthopedics, and dermatology. METHODS: Three domain-specific physicians evaluated the AI-generated therapeutic recommendations for a diverse set of 60 diseases. The evaluation criteria involved the mDISCERN score, correctness, and potential harmfulness of the recommendations. ANOVA and pairwise t tests were used to explore discrepancies in content quality and safety across models and specialties. Additionally, using the capabilities of OpenAI's most advanced model, GPT-4, an automated evaluation of each model's responses to the diseases was performed using the same criteria and compared to the physicians' assessments through Pearson correlation analysis. RESULTS: Claude-instant-v1.0 emerged with the highest mean mDISCERN score (3.35, 95% CI 3.23-3.46). In contrast, Bloomz lagged with the lowest score (1.07, 95% CI 1.03-1.10). Our analysis revealed significant differences among the models in terms of quality (P<.001). Evaluating their reliability, the models displayed strong contrasts in their falseness ratings, with variations both across models (P<.001) and specialties (P<.001). Distinct error patterns emerged, such as confusing diagnoses; providing vague, ambiguous advice; or omitting critical treatments, such as antibiotics for infectious diseases. Regarding potential harm, GPT-3.5-Turbo was found to be the safest, with the lowest harmfulness rating. All models lagged in detailing the risks associated with treatment procedures, explaining the effects of therapies on quality of life, and offering additional sources of information. Pearson correlation analysis underscored a substantial alignment between physician assessments and GPT-4's evaluations across all established criteria (P<.01). CONCLUSIONS: This study, while comprehensive, was limited by the involvement of a select number of specialties and physician evaluators. The straightforward prompting strategy ("How to treat…") and the assessment benchmarks, initially conceptualized for human-authored content, might have potential gaps in capturing the nuances of AI-driven information. The LLMs evaluated showed a notable capability in generating valuable medical content; however, evident lapses in content quality and potential harm signal the need for further refinements. Given the dynamic landscape of LLMs, this study's findings emphasize the need for regular and methodical assessments, oversight, and fine-tuning of these AI tools to ensure they produce consistently trustworthy and clinically safe medical advice. Notably, the introduction of an auto-evaluation mechanism using GPT-4, as detailed in this study, provides a scalable, transferable method for domain-agnostic evaluations, extending beyond therapy recommendation assessments.

Artificial Intelligence in Medical Education: Comparative Analysis of ChatGPT, Bing, and Medical Students in Germany.

BACKGROUND: Large language models (LLMs) have demonstrated significant potential in diverse domains, including medicine. Nonetheless, there is a scarcity of studies examining their performance in medical examinations, especially those conducted in languages other than English, and in direct comparison with medical students. Analyzing the performance of LLMs in state medical examinations can provide insights into their capabilities and limitations and evaluate their potential role in medical education and examination preparation. OBJECTIVE: This study aimed to assess and compare the performance of 3 LLMs, GPT-4, Bing, and GPT-3.5-Turbo, in the German Medical State Examinations of 2022 and to evaluate their performance relative to that of medical students. METHODS: The LLMs were assessed on a total of 630 questions from the spring and fall German Medical State Examinations of 2022. The performance was evaluated with and without media-related questions. Statistical analyses included 1-way ANOVA and independent samples t tests for pairwise comparisons. The relative strength of the LLMs in comparison with that of the students was also evaluated. RESULTS: GPT-4 achieved the highest overall performance, correctly answering 88.1% of questions, closely followed by Bing (86.0%) and GPT-3.5-Turbo (65.7%). The students had an average correct answer rate of 74.6%. Both GPT-4 and Bing significantly outperformed the students in both examinations. When media questions were excluded, Bing achieved the highest performance of 90.7%, closely followed by GPT-4 (90.4%), while GPT-3.5-Turbo lagged (68.2%). There was a significant decline in the performance of GPT-4 and Bing in the fall 2022 examination, which was attributed to a higher proportion of media-related questions and a potential increase in question difficulty. CONCLUSIONS: LLMs, particularly GPT-4 and Bing, demonstrate potential as valuable tools in medical education and for pretesting examination questions. Their high performance, even relative to that of medical students, indicates promising avenues for further development and integration into the educational and clinical landscape.

Automatic body part identification in real-world clinical dermatological images using machine learning.

BACKGROUND: Dermatological conditions are prevalent across all population sub-groups. The affected body part is of importance to their diagnosis, therapy, and research. The automatic identification of body parts in dermatological clinical pictures could therefore improve clinical care by providing additional information for clinical decision-making algorithms, discovering hard-to-treat areas, and research by identifying new patterns of disease. PATIENTS AND METHODS: In this study, we used 6,219 labelled dermatological images from our clinical database, which were used to train and validate a convolutional neural network. As a use case, qualitative heatmaps for the body part distribution in common dermatological conditions was generated using this system. RESULTS: The algorithm reached a mean balanced accuracy of 89% (range 74.8%-96.5%). Non-melanoma skin cancer photos were mostly of the face and torso, while hotspots of eczema and psoriasis image distribution included the torso, legs, and hands. CONCLUSIONS: The accuracy of this system is comparable to the best to-date published algorithms for image classification challenges, suggesting this algorithm could boost diagnosis, therapy, and research of dermatological conditions.

Outlier detection in dermatology: Performance of different convolutional neural networks for binary classification of inflammatory skin diseases.

BACKGROUND: Artificial intelligence (AI) and convolutional neural networks (CNNs) represent rising trends in modern medicine. However, comprehensive data on the performance of AI practices in clinical dermatologic images are non-existent. Furthermore, the role of professional data selection for training remains unknown. OBJECTIVES: The aims of this study were to develop AI applications for outlier detection of dermatological pathologies, to evaluate CNN architectures' performance on dermatological images and to investigate the role of professional pre-processing of the training data, serving as one of the first anchor points regarding data selection criteria in dermatological AI-based binary classification tasks of non-melanoma pathologies. METHODS: Six state-of-the-art CNN architectures were evaluated for their accuracy, sensitivity and specificity for five dermatological diseases and using five data subsets, including data selected by two dermatologists, one with 5 and the other with 11 years of clinical experience. RESULTS: Overall, 150 CNNs were evaluated on up to 4051 clinical images. The best accuracy was reached for onychomycosis (accuracy = 1.000), followed by bullous pemphigoid (accuracy = 0.951) and lupus erythematosus (accuracy = 0.912). The CNNs InceptionV3, Xception and ResNet50 achieved the best accuracy in 9, 8 and 6 out of 25 data sets, respectively (36.0%, 32.0% and 24.0%). On average, the data set provided by the senior physician and the data set provided in accordance with both dermatologists performed the best (accuracy = 0.910). CONCLUSIONS: This AI approach for the detection of outliers in dermatological diagnoses represents one of the first studies to evaluate the performance of different CNNs for binary decisions in clinical non-dermatoscopic images of a variety of dermatological diseases other than melanoma. The selection of images by an experienced dermatologist during pre-processing had substantial benefits for the performance of the CNNs. These comparative results might guide future AI approaches to dermatology diagnostics, and the evaluated CNNs might be applicable for the future training of dermatology residents.

A new way forward? Examining the potential of quantitative analysis of IgE datasets.

BACKGROUND: Allergies constitute an important public health problem, and epidemiological data is crucial to developing strategies for its prevention and therapy. Few population-based studies are available for data on allergies and sensitization. However, as these studies are expensive and time-consuming, novel approaches are searched for. OBJECTIVES: A large monocentric IgE dataset was used to analyse quantitative sensitization data in different age and gender groups and compared the results to available epidemiological data. METHODS: A total of 14,370 patients who sought medical care at the Department for Dermatology and Allergology at the Technical University of Munich, Germany was analysed. Total IgE and sensitization measured in specific IgE levels to common food allergens and aeroallergens were compared between females and males, age groups, and the year of testing (2003-2021). RESULTS: 8283 females (57.6%) and 6087 males (42.4%) were tested. The average number of specific IgE tests per patient was 12.3 ± 11.4. Total IgE increased after birth with age and reached a peak between 4-6 years in males and 10-12 years in females. Males had higher specific IgE for all common aeroallergens (house dust mite, birch, mugwort and timothy grass pollen) and food allergens (milk protein, chicken egg white, peanut, wheat flour, cod) except for cat epithelia. Data closely reflected results of population-based studies in the literature. CONCLUSION: This study shows that, despite potential patient and test selection bias, the results of the quantitative IgE-dataset analysis closely reflect results of population-based data. Thus, as large cohorts can be examined with a minute amount of effort, this surrogate method appears promising to supplement epidemiology research.

Sociodemographic, clinical and therapeutic factors as predictors of life quality impairment in psoriasis: A cross-sectional study in Italy.

Psoriasis is a chronic inflammatory skin disease showing a high burden due to its aesthetic, social, psychological, and quality of life (QoL) implications which also affect patient-physician relationship and, consequently, the adherence to treatments. Limited data on the natural history of psoriasis and factors predicting its prognosis are available. The aim of this study was to investigate patients' global characteristics, including treatments, associated with QoL impairment in psoriasis. Questionnaires evaluating sociodemographic features and Dermatology Life Quality Index (DLQI) were administered to patients. Multiple regression analysis was performed to evaluate factors associated with a large effect on patient's life (DLQI > 10), moderate effect on patient's life (DLQI ≥ 6 ≤ 10), small effect on patient's life (DLQI ≥ 2 < 6), and no effect on patient's life (DLQI < 2). Overall, 1052 consecutive patients affected by mild-to-severe psoriasis were recruited. Our logistic regression analysis showed that the influencing factors for a large effect on QoL were living in Southern Italy, depression, psoriatic arthritis, and psoriasis localization on facial, intertriginous, palmoplantar, trunk and scalp regions. For a moderate effect on patient's life, phototherapy and non-biological systemic therapies resulted to be the predictive factors. Mild psoriasis, living in social housing and the isolated involvement of scalp psoriasis had a small effect on QoL. Lastly, mild psoriasis and current biological therapies including anti-IL-12/23, anti-IL-17, and anti-TNF-α were positively associated with no life quality impairment. Perceived quality of life impairment in psoriasis not only depends on the skin disease but rather on patients' global characteristics. Therefore, the individual background of these patients should be respected in the selection of treatment options.

Visualising the past to plan the future: a network analysis of the largest European dermatology conference.

BACKGROUND: The annual conference of the European Academy of Dermatology and Venereology is one the largest dermatology conferences worldwide. OBJECTIVES: Network analysis can be used for in-depth insight into trending topics and underlying trends at the congress. MATERIALS & METHODS: Network analysis was employed to assess the entirety of the submitted abstracts to the congress in 2019. The data were processed, analysed, and visualised using easy-to-understand network graphs. Topics were then compared to their respective global burden (Disease Adjusted Life Years [DALYs]) and the number of respective publications on PubMed in the year 2018. RESULTS: Overall, 1,280 lecture titles and 1,941 poster titles were included in the final analysis. The most frequently used terms were "patients" (n = 473), "treatment" (n = 301), and "psoriasis" (n = 335). Relative to DALYs, "psoriasis" (+21.9%) among others, was rather over-represented, while "fungal skin diseases" (-7.6%) and "urticaria" (-6.4%) were under-represented. Compared to the relative number of PubMed publications in 2018, "psoriasis" (+20.3%), "acne" (+7.9%), and "alopecia" (+3.1%) were over-represented, while "melanoma" (-22.5%), "dermatitis" (-4.2%) and "pruritus" (-3.4%) were rather under-represented. CONCLUSION: The network analysis showed that the congress was a patient and therapy-centred event. An explanation for the particular focus on chronic inflammatory skin diseases and melanoma would be the introduction of new therapies at the congress. To delineate trends over time, a longitudinal network analysis including several congresses should be conducted and could be used to determine additional topics to be included in future events.

What's driving dermatology? Contribution title analysis of the largest German Dermatology Congress 2019.

BACKGROUND: Every two years, German-speaking dermatologic specialist groups gather in Berlin to share the latest developments at Germanýs largest dermatologic conference, the Annual Meeting of the Germany Society of Dermatology (DDG). Because this conference has a lasting effect on dermatologic practice and research, understanding what is moving the specialist groups means understanding what is driving dermatology in Germany. METHODS: We used word network analysis to compile and visualize the information embedded in the contribution titles to the DDG Annual Meeting in 2019. We extracted words, contributing cities and inter-connections. The data was standardized, visualized using network graphs and analyzed using common network analysis parameters. RESULTS: A total of 5509 words were extracted from 1150 contribution titles. The most frequently used words were "therapy", "patients", and "psoriasis". The highest number of contributions came from Hamburg, Berlin and Munich. High diversity in research topics was found, as well as a well-connected research network. CONCLUSIONS: Focus of the well-connected German-speaking dermatology community meeting 2019 was patient and therapy centered and lies especially on the diseases psoriasis and melanoma. Network graph analysis can provide helpful insights and help planning future congresses. It can facilitate the choice which contributors to include as imbalances become apparent. Moreover, it can help distributing the topics more evenly across the whole dermatologic spectrum.

Trends and Perspectives for Dermatological Research in Europe: An Abstract Title Analysis of ESDR and IID Congresses 2010-2019.

To better understand and interpret the trends in cutaneous research, we carried out a network analysis of all the titles of the submitted abstracts of the annual meetings of the European Society of Dermatological Research (ESDR), including the International Investigative Dermatology (IID) meetings between 2010 and 2019. Network analysis is a data science tool to process, analyze, and visualize big sets of data. As expected, psoriasis was the frontrunner in each of the annual meetings, followed by dermatitis and melanoma. Interestingly, alopecia, acne, squamous cell carcinoma, pruritus, basal cell carcinoma, and hidradenitis suppurativa were among the next most frequently named diseases and/or terms. We also looked at diversity to assess how broad the interest of the submitting community is and to identify whether "blockbusters" such as psoriasis and atopic dermatitis expand in expense of other interests. In contrast to our expectations, the diversity of submissions to the ESDR annual meetings remained high over the 10 years of our observation period. Interestingly, the diversity increased in the years of the IID, indicating an outreach to other research areas worldwide compared with the ESDR meetings. This is true for both 2013 in Edinburgh, UK, and 2018 in Orlando, USA. During these meetings, this rise in diversity was associated with a relative decrease of the three most often named diseases. Network analysis thus may be a useful tool for research societies like the ESDR to identify trends and allocate resources such as reviewers and sessions accordingly. In addition, it can serve as quality control monitoring whether the ESDR continues to offer a platform for all researchers in cutaneous biology or implements or focuses on emerging fields.

Perceptions of Digital Health Education Among European Medical Students: Mixed Methods Survey.

BACKGROUND: Digital health technologies hold promise to enhance patient-related outcomes, to support health care staff by reducing their workload, and to improve the coordination of care. As key users of digital health technologies, health care workers are crucial to enable a meaningful digital transformation of health care. Digital health literacy and digital skills should become prerequisite competencies for health professionals to facilitate the implementation and leverage the potential of digital technologies to improve health. OBJECTIVE: We aimed to assess European medical students' perceived knowledge and opinions toward digital health, the status of digital health implementation in medical education, and the students' most pressing needs. METHODS: The explanatory design of our mixed methods study was based on an online, anonymous, self-administered survey targeted toward European medical students. A linear regression analysis was used to identify the influence of the year of medical studies on the responses. Additional analysis was performed by grouping the responses by the self-evaluated frequency of eHealth technology use. Written responses to four qualitative questions in the survey were analyzed using an inductive approach. RESULTS: The survey received a total of 451 responses from 39 European countries, and there were respondents for every year of medical studies. The majority of respondents saw advantages in the use of digital health. While 40.6% (183/451) felt prepared to work in a digitized health care system, more than half (240/451, 53.2%) evaluated their eHealth skills as poor or very poor. Medical students considered lack of education to be the reason for this, with 84.9% (383/451) agreeing or strongly agreeing that more digital health education should be implemented in the medical curriculum. Students demanded introductory and specific eHealth courses covering data management, ethical aspects, legal frameworks, research and entrepreneurial opportunities, role in public health and health systems, communication skills, and practical training. The emphasis lay on tailoring learning to future job requirements and interprofessional education. CONCLUSIONS: This study shows a lack of digital health-related formats in medical education and a perceived lack of digital health literacy among European medical students. Our findings indicate a gap between the willingness of medical students to take an active role by becoming key players in the digital transformation of health care and the education that they receive through their faculties.

Microglia modulation through external vagus nerve stimulation in a murine model of Alzheimer's disease.

Chronically activated microglia contribute to the development of neurodegenerative diseases such as Alzheimer's disease (AD) by the release of pro-inflammatory mediators that compromise neuronal function and structure. Modulating microglia functions could be instrumental to interfere with disease pathogenesis. Previous studies have shown anti-inflammatory effects of acetylcholine (ACh) or norepinephrine (NE), which mainly activates the ß-receptors on microglial cells. Non-invasive vagus nerve stimulation (nVNS) is used in treatment of drug-resistant depression, which is a risk factor for developing AD. The vagus nerve projects to the brainstem's locus coeruleus from which noradrenergic fibers reach to the Nucleus Basalis of Meynert (NBM) and widely throughout the brain. Pilot studies showed first signs of cognitive-enhancing effects of nVNS in AD patients. In this study, the effects of nVNS on mouse microglia cell morphology were analyzed over a period of 280 min by 2-photon laser scanning in vivo microscopy. Total branch length, average branch order and number of branches, which are commonly used indicators for the microglial activation state were determined and compared between young and old wild-type and amyloid precursor protein/presenilin-1 (APP/PS1) transgenic mice. Overall, these experiments show strong morphological changes in microglia, from a neurodestructive to a neuroprotective phenotype, following a brief nVNS in aged animals, especially in APP/PS1 animals, whereas microglia from young animals were morphologically unaffected.

Modulation of Multidrug Resistance Gene Expression by Coumarin Derivatives in Human Leukemic Cells.

The presence of multidrug resistance (MDR) in tumor cells is considered as the major cause of failure of cancer chemotherapy. The mechanism responsible for the phenomenon of multidrug resistance is explained, among others, as overexpression of membrane transporters primarily from the ABC family which actively remove cytostatics from the tumor cell. The effect of 20 coumarin derivatives on the cytotoxicity and expression of MDR1, MRP1, BCRP, and LRP genes (encoding proteins responsible for multidrug resistance) in cancer cells was analyzed in the study. The aim of this research included determination of IC10 and IC50 values of selected coumarin derivatives in the presence and absence of mitoxantrone in leukemia cells and analysis of changes in the expression of genes involved in multidrug resistance: MDR1, MRP, LRP, and BCRP after 24-hour exposure of the investigated cell lines to selected coumarins in the presence and absence of mitoxantrone in IC10 and IC50 concentrations. The designed research was conducted on 5 cell lines derived from the human hematopoietic system: CCRF/CEM, CEM/C1, HL-60, HL-60/MX1, and HL-60/MX2. Cell lines CEM/C1, HL-60/MX1, and HL-60/MX2 exhibit a multidrug resistance phenotype.

Early brain perfusion improvement after ventriculoperitoneal shunt surgery in patients with idiopathic normal pressure hydrocephalus evaluated by 99mTc-HMPAO SPECT - preliminary report.

BACKGROUND: Idiopathic normal pressure hydrocephalus (iNPH) is a clinical syndrome that consists of the triad: gait disturbance, mental deterioration and urinary incontinence associated with normal cerebrospinal fluid pressure (CSF), without pre-existing abnormalities. The most popular treatment option is surgical implantation of a shunt. Brain perfusion increase occurring months or years after successful shunt surgery is well described in the literature. Early improvement of perfusion is not well documented. Therefore, the objective of the present study was to determine patterns of brain perfusion changes 3-6 days after the ventriculoperitoneal shunting in patients with iNPH by using 99mTc-HMPAO SPECT. MATERIAL AND METHODS: Sixteen patients with iNPH (9 women, 7 men, mean age 64.1 ± 12.7 years) who underwent ventriculoperitoneal shunt surgery were included into the study group. Indications for implanting a shunt were based on clinical history, neuroimaging and CSF dynamic studies with an infusion test. Brain perfusion SPECT was performed 1-2 days before and 3-6 days after the surgical treatment. For comparison of perfusion before and after the surgery SPECT scans were assessed visually and semiquantitatively with voxel based analysis. RESULTS: No side effects were observed after the surgery. Brain perfusion improvement after shunting was observed in 10 patients (62.5%). Patterns of perfusion changes varied between patients, with combinations of different bilateral and lateralized brain regions involved. Perfusion increased in the whole brain (3 patients), in the right cerebral hemisphere (1 patient) or in the separate cerebral regions (6 patients): frontal, parietal, temporal, cerebellum, cingulate gyrus. Perfusion improvement was predominantly observed in the frontal lobes: right frontal (3 cases, 18.8%), left frontal (3 cases, 18.8%). CONCLUSIONS: Cerebral perfusion is recovered promptly after ventriculoperitoneal shunt surgery in about 60% of patients with iNPH. This improvement may be global or regional in different cerebral areas with prevalence of the frontal lobes.

Effects of Therapy in Patients Suffering from Chronic Back Pain Treated with Spinal Cord Stimulation.

Pain in the lumbosacral part of the spine in the course of degenerative disease is the most common cause of physical activity limitation in adults. Treatment includes pharmacotherapy, physiotherapy, psychotherapy, health promotion, and sometimes surgery. Surgical treatment is not always successful, and the various clinical and psychosomatic symptoms that result from surgical treatment failure are known as failed back surgery syndrome. For some patients with this condition, spinal cord stimulation can provide relief. The aim of the work was to define subjective and objective spinal cord stimulation effects by assessing chosen disability and physical activity limitation ratios. Pain intensity, level of disability, and presence of neurological symptoms were assessed. The examination was performed twice: before the stimulator implantation and at least 6 months postimplantation. The study was conducted at the Department of Neurosurgery and Paediatric Neurosurgery in Lublin. Thirty-six patients suffering from failed back surgery syndrome were recruited for this study. The Visual Analog Scale, modified Laitinen's pain questionnaire, and Oswestry Disability Index were used in this work. The study showed that spinal cord stimulation was effective in treating spinal and lower limb pain in 64% of patients, similar to results obtained in other departments. Although back pain and neuropathic pain radiating to the lower limbs decreased, moderate physical activity impairment was still observed according to the Oswestry Disability Index scale. The decrease in neuropathic pain radiating to the lower limbs had the most significant influence on reducing physical activity impairment.

[Operative treatment of syringomyelia]. / Leczenie operacyjne jamistosci rdzenia.

There are many methods applied in operative treatment of syringomyelia. They may be divided into three groups: suboccipital decompression, myelotomy with drainage of the syringomyelic cavity, and terminal ventriculostomy. Occipito-vertebral decompression is the treatment of choice in cases of coexisting abnormalities in the region of the occipital foramen. It is also used in cases of syringomyelia without these anomalies. Multiplicity of proposed operative techniques concerns the depth of penetration in the posterior cavity, the width of bony opening and kind of material used for the duroplasty. Myelotomy with syringomyelic cavity drainage is recommended in cases of syringomyelia caused by local traumatic or inflammatory injury of the spine. It is also applied as a supplementary procedure in cases of unsuccessful occipito-vertebral decompression. Terminal ventriculostomy may be recommended in cases of anchoring of the spine and in cases of cavities located in lower parts of the spine and descending to the filum terminale.

[Diagnostics of syringomyelia coexisting with Arnold-Chiari syndrome]. / Diagnostyka jamistosci rdzenia wspólistniejacej z zespolem Arnolda-Chiariego.

Frequent coexistence of syringomyelia and Arnold-Chiari syndrome results in overlapping of symptoms caused by these processes. Because the most common location of syringomyelic cavities is in the cervico-thoracic part of the spine, the first symptoms usually concern the upper extremities. Symptoms induced by compression through the invaginated cerebellar tonsils to the occipital foramen in Arnold-Chiari syndrome may be divided into three groups: increased intracranial pressure, cerebellar and spinal symptoms. The radiological investigations applied in recognition of those cases were initially based on different kinds of myelography, then on computed tomography. Magnetic resonance imaging is the investigation of choice nowadays. It allows one to determine the location and extension of the cavities within the spine, their relation to particular anatomical structures and possible existence of partitions, and demonstrate anomalies in the region of the craniocervical junction.