Direct Measurement of the Ratio of Carbon Monoxide to Molecular Hydrogen in the Diffuse Interstellar Medium

We have used archival far-ultraviolet spectra from observations made by HST/STIS and FUSE to determine the column densities and rotational excitation temperatures for CO and H2, respectively, along the lines of sight to 23 Galactic O and B stars. The sightlines have reddening values in the range E(B-V)= 0.07-0.62, sampling the diffuse to translucent interstellar medium. We find that the H2 column densities range from 5x10^18-8x10^20 cm^-2 and the CO from upper limits around 2x10^12 cm^-2 to detections as high as 1.4x10^16 cm^-2. CO increases with increasing H2, roughly following a power law of factor \~2. The CO/H2 column density ratio is thus not constant, and ranges from 10^-7 - 10^-5, with a mean value of 3x10^-6. The sample segregates into "diffuse" and "translucent" regimes, the former having a molecular fraction less than ~0.25 and A_V/d<1 mag kpc^-1. The mean CO/H2 for these two regimes are 3.6x10^-7 and 9.3x10^-6, respectively, significantly lower than the canonical dark cloud value of 10^-4. In six of the sightlines, 13CO is observed, and the isotopic ratio we observe (~50-70) is consistent with, if perhaps a little below, the average 12C/13C for the ISM at large. The average H2 rotational excitation temperature is 74+/-24 K, in good agreement with previous studies, and the average CO temperature is 4.1 K, with some sightlines as high as 6.4 K. The higher excitation CO is observed with higher column densities, consistent with the effects of photon trapping in clouds with densities in the 20-100 cm^-3 range. We discuss the implications for the structure of the diffuse/translucent regimes of the interstellar medium and the estimation of molecular mass in galaxies.Comment: emualateapj style, 6 figures, 3 tables, accepted on 21 Nov 2006 for publication in The Astrophysical Journa

Clinical biomarker innovation: when is it worthwhile?

Contains fulltext : 208980.pdf (publisher's version ) (Open Access

New clinical prediction model for early recognition of sepsis in adult primary care patients:a prospective diagnostic cohort study of development and external validation

Background Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs.Aim To develop and validate a sepsis prediction model for adult patients in primary care.Design and setting This was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020.Method Adult patients who were acutely ill and received home visits were included. A total of nine clinical variables were selected as candidate predictors, next to the biomarkers C-reactive protein, procalcitonin, and lactate. The primary endpoint was sepsis within 72 hours of inclusion, as established by an expert panel. Multivariable logistic regression with backwards selection was used to design an optimal model with continuous clinical variables. The added value of the biomarkers was evaluated. Subsequently, a simple model using single cut-off points of continuous variables was developed and externally validated in two emergency department populations.Results A total of 357 patients were included with a median age of 80 years (interquartile range 71–86), of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (aged &gt;65 years; temperature &gt;38°C; systolic blood pressure ≤110 mmHg; heart rate &gt;110/min; saturation ≤95%; and altered mental status) had good discrimination and calibration (C-statistic of 0.80 [95% confidence interval = 0.75 to 0.84]; Brier score 0.175). Biomarkers did not improve the performance of the model and were therefore not included. The model was robust during external validation.Conclusion Based on this study’s GP out-of-hours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Evaluation of prediction models and diagnostic tests

Evaluation of diagnostic tests may seem a straightforward practice at first sight, but unfortunately complex methods are often required. The goal of this thesis is to assess methodological challenges surrounding the evaluation and impact of diagnostic tests and prediction models, and to propose alternative approaches to reduce bias, miscommunication, and research waste. The first part addresses problems which may occur when expert panels are used as a reference standard in diagnostic evaluation research. There we found that dichotomous target disease classification by expert panels leads to incorrect estimation of diagnostic accuracy of the test under evaluation. Alternatives, such as obtaining the probability of target disease presence from experts panels for each individual, could partially resolve this, but are strongly dependent on assumptions that are made. In the second part of this thesis we explore the use of terminology related to overdiagnosis, overtesting, and overmedicalisation in scientific literature. We found that these are described across virtually all clinical domains, however they are used inconsistently. In response, we created a framework that can be used to describe overdiagnosis and related concepts within clinical domains, and provide strategies for reducing these. The last part of this thesis focusses on evaluation of impact of diagnostic tests and prediction models on health and monetary outcomes. It has been shown that many prediction models don’t result in their anticipated impact. We demonstrate how decision analytic models can be used to identify specific factors and determine how they influence potential outcomes before conducting a clinical study

Evaluation of prediction models and diagnostic tests

Evaluation of diagnostic tests may seem a straightforward practice at first sight, but unfortunately complex methods are often required. The goal of this thesis is to assess methodological challenges surrounding the evaluation and impact of diagnostic tests and prediction models, and to propose alternative approaches to reduce bias, miscommunication, and research waste. The first part addresses problems which may occur when expert panels are used as a reference standard in diagnostic evaluation research. There we found that dichotomous target disease classification by expert panels leads to incorrect estimation of diagnostic accuracy of the test under evaluation. Alternatives, such as obtaining the probability of target disease presence from experts panels for each individual, could partially resolve this, but are strongly dependent on assumptions that are made. In the second part of this thesis we explore the use of terminology related to overdiagnosis, overtesting, and overmedicalisation in scientific literature. We found that these are described across virtually all clinical domains, however they are used inconsistently. In response, we created a framework that can be used to describe overdiagnosis and related concepts within clinical domains, and provide strategies for reducing these. The last part of this thesis focusses on evaluation of impact of diagnostic tests and prediction models on health and monetary outcomes. It has been shown that many prediction models don’t result in their anticipated impact. We demonstrate how decision analytic models can be used to identify specific factors and determine how they influence potential outcomes before conducting a clinical study

Supplementary files for Systematic review on Effectiveness of Contact tracing apps for COVID-19

These files are supplementary to the F1000Research systematic review publication on contact tracing apps for COVID-1