Tandem spinal stenosis: clinical pattern, diagnosis and surgical tactics

The purpose of this review is to present an up-to-date look at the features of diagnosis and treatment of tandem spinal stenosis. Tandem spinal stenosis is a degenerative narrowing of the spinal canal of two or more parts of the spine column. Diagnosis of the one-level degenerative spinal stenosis is commonly quite easy in the practice of a spinal surgeons, while the diagnosis of tandem spinal stenosis is often difficult. The clinical presentation of tandem spinal stenosis presents a constellation of different symptoms and often cause late diagnosis. Data on the prevalence of tandem spinal stenosis are very variable, and the etiology cannot be fully studied. Thereby, comprehensive assessments of the symptoms and imaging provide assistance with the accurate and timely diagnosis. The choice of surgical method should consider possibility of staged decompression of each stenotic levels with priority for proximal parts of spine column.

Traditional Chinese herbal medicine-potential therapeutic application for the treatment of COVID-19

Traditional Chinese Medicine (TCM) is a complete medical system that has evolved over millennia to include practices and procedures such as acupuncture, herbal medicine, manual therapies, nutrition, and mind–body therapies such as qi gong. In modern-day China and other Asian countries, TCM is a medical subspecialty utilized alongside western biomedicine. During the current Coronavirus Disease 19 (COVID-19) pandemic, TCM and TCM herbal medicine is being used and a number of single herbs and combination formulas have significant bioactivity and therapeutic potential. The purpose of this paper is to highlight the use of TCM in the treatment of COVID-19. This commentary provides the reader with a concise background on COVID-19 and summarizes TCM concepts including identification, pattern diagnosis, and treatment principles commonly used for the treatment of viral influenza-like diseases. It also highlights some of the challenges and potential for using TCM in an integrated medical setting.

Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods

Background Integrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge. Objective The purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms. Methods A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms. Results The Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms. Conclusions The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group’s sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods. Trial Registration ClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908

Management Trend and Attitude Regarding Osteoporotic Vertebral Compression Fracture: A Comparative Study Between Surgeons and Internists

Background: Osteoporotic vertebral compression fracture (OVCF) is the most common form of osteoporotic fracture, both surgeons and internists included in the management of that. This study aimed to identify whether a discrepancy exists between spinal surgeons and internists in the diagnosis and management of OVCF.Method: This comparative study included 124 spinal surgeons and 47 internists in the WeChat group of the Society of Osteoporosis and Bone Mineral Research. They were sent a self-administered electronic questionnaire that asked about practice pattern, diagnosis trend, and management choice in OVCF management. The validity of the survey was examined in advance.Results: A significantly higher percentage of surgeons obtained T2-weighted images scan with fat suppression than internists. A significantly higher proportion of spinal surgeons provided surgical treatment as first-line treatment and considered fracture as the most important aspect in OVCF management than internist. No significant difference was observed in the use of dual-energy X-ray absorptiometry scan, in performing laboratory examination, or in the collaboration rate between the two groups.Conclusion: Differences exist between internists and spinal surgeons in imaging diagnosis, choice of therapeutic schedule, and attitude to osteoporosis treatment in the management of OVCF.

Linear Atrophoderma of Moulin Localized to Face: An Exceedingly Rare Entity

Introduction: Linear atrophoderma of Moulin (LAM) is a rare dermatologic disorder characterized by hyperpigmented atrophic plaques following the Blaschko lines (BL). The trunk and limbs are the usual sites affected. Isolated facial involvement is an exceedingly rare entity. Despite a comprehensive medical literature search, the author could find only two cases of LAM where the lesions are exclusively localized to the face. In this article, the author presents the third case of LAM localized to face only. Case Presentation: A 26-year-old male complained of multiple linear non-pruritic pigmented lesions over the left side of the nose and glabellar area of six months’ duration. There was no history of erythema, thickening/hardening of skin, or violaceous border surrounding the lesions. On clinical examination, there were multiple hyperpigmented brownish lesions, the majority of which were depressed, involving the left ala and bridge of nose laterally and glabellar area in a Blaschkoid pattern. Diagnosis of LAM was established based on suggestive history and clinical examination. Conclusions: LAM is a rare disorder, and the facial localization makes it exceedingly rare. It should be kept in the differential diagnosis when hyperpigmented depressed lesions are present in a Blaschkoid pattern on the face.

Cerebral Palsy is 25 or More of Hypoxia Index and No Cerebral Palsy is 24 or Less of Index in Fetal Monitoring

Fetal outcome was ominous if fetal heart rate (FHR) was late deceleration (LD) in the past, while 3 connected typical LDs were normal, and repeated LDs for 50 min were heavy fetal brain damage. Also, LD is defined as LD when it is repeated for 15 minutes. As the fetus is damaged by repeated hypoxic decelerations followed by cerebral palsy, but not by its late appearing in LD, novel fetal hypoxia index (HI) is the sum of all deceleration durations (min) divided by the lowest FHR (bpm) and multiplied by 100 in fetal monitoring. The hypoxia index was 25 or more in all of 6 cerebral palsy cases, while it was 24 or less in all 16 cases of no cerebral palsy. As error probability is almost zero in the chi2 test of hypoxia index, no cerebral palsy is decided when the hypoxia index is 24 or less, while it is cerebral palsy, if hypoxia index is 25 or more. The hypoxia index is adopted to all FHR decelerations and bradycardia, as hypoxia index does not evaluate the late appearing of deceleration, instead of past subjective deceleration pattern diagnosis in fetal monitoring.

Cerebral Palsy is 25 or More of Hypoxia Index and No Cerebral Palsy is 24 or Less of Index in Fetal Monitoring

Fetal outcome was ominous if fetal heart rate (FHR) was late deceleration (LD) in the past, while 3 connected typical LDs were normal, and repeated LDs for 50 min were heavy fetal brain damage. Also, LD is defined as LD when it is repeated for 15 minutes. As the fetus is damaged by repeated hypoxic decelerations followed by cerebral palsy, but not by its late appearing in LD, novel fetal hypoxia index (HI) is the sum of all deceleration durations (min) divided by the lowest FHR (bpm) and multiplied by 100 in fetal monitoring. The hypoxia index was 25 or more in all of 6 cerebral palsy cases, while it was 24 or less in all 16 cases of no cerebral palsy. As error probability is almost zero in the chi2 test of hypoxia index, no cerebral palsy is decided when the hypoxia index is 24 or less, while it is cerebral palsy, if hypoxia index is 25 or more. The hypoxia index is adopted to all FHR decelerations and bradycardia, as hypoxia index does not evaluate the late appearing of deceleration, instead of past subjective deceleration pattern diagnosis in fetal monitoring.

Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods (Preprint)

BACKGROUND Integrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge. OBJECTIVE The purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms. METHODS A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms. RESULTS The Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms. CONCLUSIONS The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group’s sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods. CLINICALTRIAL ClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908