The clinical utility of an elevated-risk multivariate index assay score in ovarian cancer patients.

OBJECTIVE: Early involvement of specialists is associated with improved survival in ovarian cancer patients. However, only 33-60% of patients are currently being referred. A more effective strategy to get patients with an elevated risk of ovarian cancer to gynecologic oncologists is needed. The objective of this study was to evaluate the clinical utility of a multivariate index assay, OVA1 * , by assessing its ability to drive referral of ovarian cancer patients to gynecologic oncologists prior to their first surgical intervention. RESEARCH DESIGN AND METHODS: Information on patients who received a multivariate index assay was collected retrospectively from 22 obstetricians/gynecologists through a chart review. Physicians were recruited from a variety of practices and hospitals representing major geographic regions within the United States. Clinical utility of the multivariate index assay was assessed by examining the rate at which obstetricians/gynecologists involved a gynecologic oncologist for patients with elevated-risk results prior to first surgical intervention. RESULTS: A total of 136 patients with elevated-risk assay results were assessed, of whom 122 underwent surgery to remove an adnexal mass. Prior to surgery, 98 (80%) of the patients were referred to a gynecologic oncologist with an additional 11 (9%) having a gynecologic oncologist available if required by intra-operative findings. Primary ovarian cancer was found in 65 (53%) patients, and gynecologic oncologists performed 61 (94%) of the initial surgeries these patients. Similar results were found in premenopausal and postmenopausal patients. CONCLUSIONS: A high proportion of patients with elevated-risk assay results were referred to a gynecologic oncologist prior to their initial surgery. Nearly all of the patients who had primary ovarian malignancies were appropriately referred to gynecologic oncologists, highlighting the clinical utility of the multivariate index assay. Nearly all patients identified with ovarian cancer received their initial surgery by a gynecologic oncologist - demonstrating a higher rate of gynecologic oncologist involvement in comparison to past studies. The study may be limited by unintentional bias in physician response and recall.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

The generalization of visuomotor learning to untrained movements and movement sequences based on movement vector and goal location remapping.

The planning of goal-directed movements is highly adaptable; however, the basic mechanisms underlying this adaptability are not well understood. Even the features of movement that drive adaptation are hotly debated, with some studies suggesting remapping of goal locations and others suggesting remapping of the movement vectors leading to goal locations. However, several previous motor learning studies and the multiplicity of the neural coding underlying visually guided reaching movements stand in contrast to this either/or debate on the modes of motor planning and adaptation. Here we hypothesize that, during visuomotor learning, the target location and movement vector of trained movements are separately remapped, and we propose a novel computational model for how motor plans based on these remappings are combined during the control of visually guided reaching in humans. To test this hypothesis, we designed a set of experimental manipulations that effectively dissociated the effects of remapping goal location and movement vector by examining the transfer of visuomotor adaptation to untrained movements and movement sequences throughout the workspace. The results reveal that (1) motor adaptation differentially remaps goal locations and movement vectors, and (2) separate motor plans based on these features are effectively averaged during motor execution. We then show that, without any free parameters, the computational model we developed for combining movement-vector-based and goal-location-based planning predicts nearly 90% of the variance in novel movement sequences, even when multiple attributes are simultaneously adapted, demonstrating for the first time the ability to predict how motor adaptation affects movement sequence planning.

Adaptation to dynamic environments displays local generalization for voluntary reaching movements.

The shape of the directional generalization function for adaptation to a viscous force-field environment has been controversial. Some studies have suggested wide, essentially global generalization and others have suggested narrow, local generalization. Here, we show definitively that motor adaptation displays narrow generalization with a minimal global component and a peak at the trained movement direction for both single-trial and asymptotic adaptation. Furthermore, we find that reaching movements in opposite directions do not interfere with one another during force-field learning.