Machine learning to predict sports-related concussion recovery using clinical data

ObjectivesSport-related concussions (SRCs) are a concern for high school athletes. Understanding factors contributing to SRC recovery time may improve clinical management. However, the complexity of the many clinical measures of concussion data precludes many traditional methods. This study aimed to answer the question, what is the utility of modeling clinical concussion data using machine-learning algorithms for predicting SRC recovery time and protracted recovery? MethodsThis was a retrospective case series of participants aged 8 to 18 years with a diagnosis of SRC. A 6-part measure was administered to assess pre-injury risk factors, initial injury severity, and post-concussion symptoms, including the Vestibular Ocular Motor Screening (VOMS) measure, King-Devick Test and C3 Logix Trails Test data. These measures were used to predict recovery time (days from injury to full medical clearance) and binary protracted recovery (recovery time \u3e 21 days) according to several sex-stratified machine-learning models. The ability of the models to discriminate protracted recovery was compared to a human-driven model according to the area under the receiver operating characteristic curve (AUC). ResultsFor 293 males (mean age 14.0 years) and 362 females (mean age 13.7 years), the median (interquartile range) time to recover from an SRC was 26 (18–39) and 21 (14–31) days, respectively. Among 9 machine-learning models trained, the gradient boosting on decision-tree algorithms achieved the best performance to predict recovery time and protracted recovery in males and females. The models’ performance improved when VOMS data were used in conjunction with the King-Devick Test and C3 Logix Trails Test data. For males and females, the AUC was 0.84 and 0.78 versus 0.74 and 0.73, respectively, for statistical models for predicting protracted recovery. ConclusionsMachine-learning models were able to manage the complexity of the vestibular-ocular motor system data. These results demonstrate the clinical utility of machine-learning models to inform prognostic evaluation for SRC recovery time and protracted recovery

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Lawson criterion for ignition exceeded in an inertial fusion experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion

PICO: Probe of Inflation and Cosmic Origins

The Probe of Inflation and Cosmic Origins (PICO) is an imaging polarimeter that will scan the sky for 5 years in 21 frequency bands spread between 21 and 799 GHz. It will produce full-sky surveys of intensity and polarization with a final combined-map noise level of 0.87 \u3bcK arcmin for the required specifications, equivalent to 3300 Planck missions, and with our current best-estimate would have a noise level of 0.61 \u3bcK arcmin (6400 Planck missions). PICO will either determine the energy scale of inflation by detecting the tensor to scalar ratio at a level r=5 710 124 (5\u3c3), or will rule out with more than 5\u3c3 all inflation models for which the characteristic scale in the potential is the Planck scale. With LSST's data it could rule out all models of slow-roll inflation. PICO will detect the sum of neutrino masses at >4\u3c3, constrain the effective number of light particle species with \u394Neff<0.06 (2\u3c3), and elucidate processes affecting the evolution of cosmic structures by measuring the optical depth to reionization with errors limited by cosmic variance and by constraining the evolution of the amplitude of linear fluctuations \u3c38(z) with sub-percent accuracy. Cross-correlating PICO's map of the thermal Sunyaev-Zeldovich effect with LSST's gold sample of galaxies will precisely trace the evolution of thermal pressure with z. PICO's maps of the Milky Way will be used to determine the make up of galactic dust and the role of magnetic fields in star formation efficiency. With 21 full sky legacy maps in intensity and polarization, which cannot be obtained in any other way, the mission will enrich many areas of astrophysics. PICO is the only single-platform instrument with the combination of sensitivity, angular resolution, frequency bands, and control of systematic effects that can deliver this compelling, timely, and broad science

Comparison of Prescribed Physical Therapy to a Home Exercise Program for Pediatric Sports-Related Concussion Patients

The purpose of this retrospective chart review was to compare sports-related concussion (SRC) recovery time in protracted recovery (≥28 days) patients who were prescribed physical therapy (PPT) with those who were only provided a home exercise program (HEP). We hypothesized PPT would be associated with shorter recovery times relative to HEP. Associations were evaluated with multivariable zero-truncated negative binomial regressions. Among the 48 (30.2%) PPT and 111 (69.8%) HEP patients, the majority were female (57.9%), the mean age was 15.3 ± 1.4 (PPT) and 14.2 ± 2.8 (HEP), and time to clinic was a median 6.0 (IQR = 3.0–27.0; PPT) and 7.0 (IQR = 3.0–23.0; HEP) days. After adjusting for demographic (age, sex) and clinical measures (concussion history, convergence, VOMS, PCSS score, and days to clinic), PPT unexpectedly was associated with 1.21 (95% CI: 1.05, 1.41) additional recovery days compared with HEP. One reason for this could be related to patients adhering to the number of a priori prescribed PT sessions which may or may not have aligned with the patient’s symptom resolution. Future research should explore this hypothesis while aiming to evaluate the effect of PPT versus HEP using a randomized design. If confirmed, these findings are encouraging for patients who could not otherwise access or afford specialty rehabilitation

Association Between Concussion History and Factors Relating to Cognitive, Behavioral, and Emotional Health Among American High School Athletes: A Cross-sectional Analysis

Background: The cognitive, behavioral, and emotional deficits that may be associated with sports-related concussions among adolescents are unclear. Purpose: To examine the association between reported concussion history and factors relating to cognitive, behavioral, and emotional health among a population-based sample of US high school–aged adolescents. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Participants included a representative sample of US high school student-athletes who reported a concussion history (in the past 12 months) and relevant behaviors/outcomes within 3 domains: cognitive (academics, difficulty concentrating), behavioral (drinking and driving, carrying a weapon, physically fighting, tobacco use, marijuana use, binge drinking), and emotional (symptoms of depression, suicidal thoughts/actions). The adjusted relative odds of experiencing 0 and at least 1, 2, 3, or ≥4 concussions were modeled while mutually adjusting for the behaviors of interest in addition to age, race/ethnicity, and sleep problems. Data were reported in sex stratum. Results: A total of 13,268 participants were included in the unweighted data set. Overall, 14.5% (95% CI, 12.9%-16.2%) of female and 18.1% (95% CI, 16.4%-19.8%) of male student-athletes had at least 1 concussion in the past 12 months. As compared with those who reported not engaging in any of the behaviors deleterious to health or having had any of the negative health outcomes (composite score of 0), female athletes with composite scores of 1 to 4, 5 to 7, and 8 to 11 were 1.94 (95% CI, 1.55-2.43), 3.13 (95% CI, 2.30-4.33), and 6.05 (95% CI, 3.75-9.75) times more likely to have a recent history of concussions after accounting for relevant factors. As compared with those having a composite score of 0, male athletes with composite scores of 1 to 4, 5 to 7, and 8 to 11 were 2.03 (95% CI, 1.58-2.59), 3.80 (95% CI, 2.71-5.34), and 8.23 (95% CI, 4.91-13.77) times more likely to have a recent history of concussions after accounting for relevant factors. Conclusion: Self-reported concussions among US high school athletes is related to several deleterious health behaviors and outcomes. These associations should be confirmed in longitudinal analyses