Utilization of a novel digital measurement tool for quantitative assessment of upper extremity motor dexterity: a controlled pilot study.

BackgroundThe current methods of assessing motor function rely primarily on the clinician's judgment of the patient's physical examination and the patient's self-administered surveys. Recently, computerized handgrip tools have been designed as an objective method to quantify upper-extremity motor function. This pilot study explores the use of the MediSens handgrip as a potential clinical tool for objectively assessing the motor function of the hand.MethodsEleven patients with cervical spondylotic myelopathy (CSM) were followed for three months. Eighteen age-matched healthy participants were followed for two months. The neuromotor function and the patient-perceived motor function of these patients were assessed with the MediSens device and the Oswestry Disability Index respectively. The MediSens device utilized a target tracking test to investigate the neuromotor capacity of the participants. The mean absolute error (MAE) between the target curve and the curve tracing achieved by the participants was used as the assessment metric. The patients' adjusted MediSens MAE scores were then compared to the controls. The CSM patients were further classified as either "functional" or "nonfunctional" in order to validate the system's responsiveness. Finally, the correlation between the MediSens MAE score and the ODI score was investigated.ResultsThe control participants had lower MediSens MAE scores of 8.09%±1.60%, while the cervical spinal disorder patients had greater MediSens MAE scores of 11.24%±6.29%. Following surgery, the functional CSM patients had an average MediSens MAE score of 7.13%±1.60%, while the nonfunctional CSM patients had an average score of 12.41%±6.32%. The MediSens MAE and the ODI scores showed a statistically significant correlation (r=-0.341, p<1.14×10⁻⁵). A Bland-Altman plot was then used to validate the agreement between the two scores. Furthermore, the percentage improvement of the the two scores after receiving the surgical intervention showed a significant correlation (r=-0.723, p<0.04).ConclusionsThe MediSens handgrip device is capable of identifying patients with impaired motor function of the hand. The MediSens handgrip scores correlate with the ODI scores and may serve as an objective alternative for assessing motor function of the hand

Utilization of a novel digital measurement tool for quantitative assessment of upper extremity motor dexterity: a controlled pilot study

BACKGROUND: The current methods of assessing motor function rely primarily on the clinician’s judgment of the patient’s physical examination and the patient’s self-administered surveys. Recently, computerized handgrip tools have been designed as an objective method to quantify upper-extremity motor function. This pilot study explores the use of the MediSens handgrip as a potential clinical tool for objectively assessing the motor function of the hand. METHODS: Eleven patients with cervical spondylotic myelopathy (CSM) were followed for three months. Eighteen age-matched healthy participants were followed for two months. The neuromotor function and the patient-perceived motor function of these patients were assessed with the MediSens device and the Oswestry Disability Index respectively. The MediSens device utilized a target tracking test to investigate the neuromotor capacity of the participants. The mean absolute error (MAE) between the target curve and the curve tracing achieved by the participants was used as the assessment metric. The patients’ adjusted MediSens MAE scores were then compared to the controls. The CSM patients were further classified as either “functional” or “nonfunctional” in order to validate the system’s responsiveness. Finally, the correlation between the MediSens MAE score and the ODI score was investigated. RESULTS: The control participants had lower MediSens MAE scores of 8.09%±1.60%, while the cervical spinal disorder patients had greater MediSens MAE scores of 11.24%±6.29%. Following surgery, the functional CSM patients had an average MediSens MAE score of 7.13%±1.60%, while the nonfunctional CSM patients had an average score of 12.41%±6.32%. The MediSens MAE and the ODI scores showed a statistically significant correlation (r=-0.341, p<1.14×10(-5)). A Bland-Altman plot was then used to validate the agreement between the two scores. Furthermore, the percentage improvement of the the two scores after receiving the surgical intervention showed a significant correlation (r=-0.723, p<0.04). CONCLUSIONS: The MediSens handgrip device is capable of identifying patients with impaired motor function of the hand. The MediSens handgrip scores correlate with the ODI scores and may serve as an objective alternative for assessing motor function of the hand. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1743-0003-11-121) contains supplementary material, which is available to authorized users

Role of Immune Checkpoint Inhibitors in Gastrointestinal Malignancies

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of several solid and hematological malignancies. ICIs are not only able to produce long and durable responses, but also very well tolerated by patients. There are several approved indications of use of ICIs in treatment of metastatic gastrointestinal malignancies including gastric, esophageal, colorectal and hepatocellular carcinoma. In addition, ICIs can be used in microsatellite instability-high (MSI-H) and high tumor mutational burden (TMB) tumors in chemotherapy-resistant setting. Despite having good efficacy and superior safety profile, ICIs are clinically active in small subset of patients, therefore, there is a huge unmet need to enhance their efficacy and discover new predictive biomarkers. There are several ongoing clinical trials that are exploring the role of ICIs in various gastrointestinal cancers either as single agent or in combination with chemotherapy, radiation therapy, targeted agents or other immunotherapeutic agents. In this review, we discuss the published and ongoing trials for ICIs in gastrointestinal malignancies, including esophageal, gastric cancer, pancreatic, hepatocellular, biliary tract, colorectal and anal cancers. Specifically, we focus on the use of ICIs in each line of therapy and discuss the future directions of these agents in each type of gastrointestinal cancer

Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2) = 0.452; MAD = 0.0887; p = 1.17 × 10(−3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2) = 0.932; MAD = 0.0283; p = 5.73 × 10(−12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate