The value of clinical judgement analysis for improving the quality of doctors' prescribing decisions

Background Many initiatives are taken to improve prescribing decisions. Educational strategies for doctors have been effective in at least 50% of cases. Some reflection on one's own performance seems to be a common feature of the most effective strategies. So far, such reflections have mainly focused on the observed outcomes of the doctors' decisions, i.e. on what doctors do in practice. Studies in other fields have shown that another form of feedback based on the analysis of judgements may be useful as well. Objectives The objectives of the study were to discuss the principles underlying clinical judgement analysis, give examples of its use in the medical context, and discuss its potential for improving prescribing decisions. Results Clinical judgement analysis can look behind the outcome of a decision to the underlying decision process. Carefully constructed or selected case material is required for this analysis. Combining feedback on outcomes with feedback based on clinical judgement analysis offers doctors insight both in what they do, and why or when they do it. It may reveal determinants of decision making which are not available through unaided introspection. Interventions using this combination of feedback for improving doctors' prescribing behaviour have been (partly) successful in 4 cases and unsuccessful in one case. Conclusions Clinical judgement analysis gives doctors a structured reflection on the decision-making policy, and can help them to improve their future decisions. It may be especially useful for groups of doctors who try to work towards a consensus policy. The approach is not very helpful when simple decision rules are appropriate

De-noising of galaxy optical spectra with autoencoders

Optical spectra contain a wealth of information about the physical properties and formation histories of galaxies. Often though, spectra are too noisy for this information to be accurately retrieved. In this study, we explore how machine learning methods can be used to de-noise spectra and increase the amount of information we can gain without having to turn to sample averaging methods such as spectral stacking. Using machine learning methods trained on noise-added spectra - SDSS spectra with Gaussian noise added - we investigate methods of maximising the information we can gain from these spectra, in particular from emission lines, such that more detailed analysis can be performed. We produce a variational autoencoder (VAE) model, and apply it on a sample of noise-added spectra. Compared to the flux measured in the original SDSS spectra, the model values are accurate within 0.3-0.5 dex, depending on the specific spectral line and S/N. Overall, the VAE performs better than a principle component analysis (PCA) method, in terms of reconstruction loss and accuracy of the recovered line fluxes. To demonstrate the applicability and usefulness of the method in the context of large optical spectroscopy surveys, we simulate a population of spectra with noise similar to that in galaxies at z = 0.1 observed by the Dark Energy Spectroscopic Instrument (DESI). We show that we can recover the shape and scatter of the MZR in this ‘DESI-like’ sample, in a way that is not possible without the VAE-assisted de-noising

The recent star formation history of NGC 628 on resolved scales

Star formation histories (SFHs) are integral to our understanding of galaxy evolution. We can study recent SFHs by comparing the star formation rate (SFR) calculated using different tracers, as each probes a different timescale. We aim to calibrate a proxy for the present-day rate of change in SFR, dSFR/dt, which does not require full spectral energy distribution (SED) modeling and depends on as few observables as possible, to guarantee its broad applicability. To achieve this, we create a set of models in CIGALE and define a SFR change diagnostic as the ratio of the SFR averaged over the past 5 and 200 Myr, ⟨SFR5⟩/⟨SFR200⟩⁠, probed by the Hα −FUV colour. We apply ⟨SFR5⟩/⟨SFR200⟩ to the nearby spiral NGC 628 and find that its star formation activity has overall been declining in the recent past, with the spiral arms, however, maintaining a higher level of activity. The impact of the spiral arm structure is observed to be stronger on ⟨SFR5⟩/⟨SFR200⟩ than on the star formation efficiency (SFEH2⁠). In addition, increasing disk pressure tends to increase recent star formation, and consequently ⟨SFR5⟩/⟨SFR200⟩⁠. We conclude that ⟨SFR5⟩/⟨SFR200⟩ is sensitive to the molecular gas content, spiral arm structure, and disk pressure. The ⟨SFR5⟩/⟨SFR200⟩ indicator is general and can be used to reconstruct the recent SFH of any star-forming galaxy for which Hα, FUV, and either mid- or far-IR photometry is available, without the need of detailed modeling

De-noising of galaxy optical spectra with autoencoders

Optical spectra contain a wealth of information about the physical properties and formation histories of galaxies. Often though, spectra are too noisy for this information to be accurately retrieved. In this study, we explore how machine learning methods can be used to de-noise spectra and increase the amount of information we can gain without having to turn to sample averaging methods such as spectral stacking. Using machine learning methods trained on noise-added spectra - SDSS spectra with Gaussian noise added - we investigate methods of maximising the information we can gain from these spectra, in particular from emission lines, such that more detailed analysis can be performed. We produce a variational autoencoder (VAE) model, and apply it on a sample of noise-added spectra. Compared to the flux measured in the original SDSS spectra, the model values are accurate within 0.3-0.5 dex, depending on the specific spectral line and S/N. Overall, the VAE performs better than a principle component analysis (PCA) method, in terms of reconstruction loss and accuracy of the recovered line fluxes. To demonstrate the applicability and usefulness of the method in the context of large optical spectroscopy surveys, we simulate a population of spectra with noise similar to that in galaxies at $z = 0.1$ observed by the Dark Energy Spectroscopic Instrument (DESI). We show that we can recover the shape and scatter of the MZR in this "DESI-like" sample, in a way that is not possible without the VAE-assisted de-noising.Comment: 14 pages, 10 figures, 6 tables, accepted for publication in MNRA

Clinical Judgment Analysis

SUMMARY Judgment is central to the practice of medicine and occurs between making clinical observations and taking clinical decisions. Clinical judgment analysis has developed as a method of making statistically firm models of doctors' judgments. Computed models reveal the differential importance attached to items of clinical, social, or other data which are determinants of clinical decisions. These models can both reveal the causes of conflicts of judgment and may help resolve them in a way that unaided discussion cannot. Revealing experts' models to students speeds learning of diagnostic skills. Clinical judgment analysis offers a method of probing the judgments not just of students and doctors but also of patients who have shown systematic differences in their perceptions of risk and benefit. The power and relevance of clinical trials can be improved by the consistent application of judgment policies generated from both the trialists and those who will use their result

A deep search for molecular gas in two massive Lyman break galaxies at z=3 and 4: vanishing CO-emission due to low metallicity

We present deep IRAM Plateau de Bure Interferometer (PdBI) observations, searching for CO-emission toward two massive, non-lensed Lyman break galaxies (LBGs) at z=3.216 and 4.058. With one low significance CO detection (3.5 sigma) and one sensitive upper limit, we find that the CO lines are >~ 3-4 times weaker than expected based on the relation between IR and CO luminosities followed by similarly, massive galaxies at z=0-2.5. This is consistent with a scenario in which these galaxies have low metallicity, causing an increased CO-to-H_2 conversion factor, i.e., weaker CO-emission for a given molecular (H_2) mass. The required metallicities at z>3 are lower than predicted by the fundamental metallicity relation (FMR) at these redshifts, consistent with independent evidence. Unless our galaxies are atypical in this respect, detecting molecular gas in normal galaxies at z>3 may thus remain challenging even with ALMA

Geometrical tests of cosmological models. III. The cosmology-evolution diagram at z=1

The rotational velocity of distant galaxies, when interpreted as a size (luminosity) indicator, may be used as a tool to select high redshift standard rods (candles) and probe world models and galaxy evolution via the classical angular diameter-redshift or Hubble diagram tests. We implement the proposed testing strategy using a sample of 30 rotators spanning the redshift range 0.2<z<1 with high resolution spectra and images obtained by the VIMOS/VLT Deep Redshift Survey (VVDS) and the Great Observatories Origins Deep Survey (GOODs). We show that by applying at the same time the angular diameter-redshift and Hubble diagrams to the same sample of objects (i.e. velocity selected galactic discs) one can derive a characteristic chart, the cosmology-evolution diagram, mapping the relation between global cosmological parameters and local structural parameters of discs such as size and luminosity. This chart allows to put constraints on cosmological parameters when general prior information about discs evolution is available. In particular, by assuming that equally rotating large discs cannot be less luminous at z=1 than at present (M(z=1) < M(0)), we find that a flat matter dominated cosmology (Omega_m=1) is excluded at a confidence level of 2sigma and an open cosmology with low mass density (Omega_m = 0.3) and no dark energy contribution is excluded at a confidence level greater than 1 sigma. Inversely, by assuming prior knowledge about the cosmological model, the cosmology-evolution diagram can be used to gain useful insights about the redshift evolution of the structural parameters of baryonic discs hosted in dark matter halos of nearly equal masses.Comment: 14 pages and 11 figures. A&A in pres

Galaxy Zoo and ALFALFA: Atomic Gas and the Regulation of Star Formation in Barred Disc Galaxies

We study the observed correlation between atomic gas content and the likelihood of hosting a large scale bar in a sample of 2090 disc galaxies. Such a test has never been done before on this scale. We use data on morphologies from the Galaxy Zoo project and information on the galaxies' HI content from the ALFALFA blind HI survey. Our main result is that the bar fraction is significantly lower among gas rich disc galaxies than gas poor ones. This is not explained by known trends for more massive (stellar) and redder disc galaxies to host more bars and have lower gas fractions: we still see at fixed stellar mass a residual correlation between gas content and bar fraction. We discuss three possible causal explanations: (1) bars in disc galaxies cause atomic gas to be used up more quickly, (2) increasing the atomic gas content in a disc galaxy inhibits bar formation, and (3) bar fraction and gas content are both driven by correlation with environmental effects (e.g. tidal triggering of bars, combined with strangulation removing gas). All three explanations are consistent with the observed correlations. In addition our observations suggest bars may reduce or halt star formation in the outer parts of discs by holding back the infall of external gas beyond bar co-rotation, reddening the global colours of barred disc galaxies. This suggests that secular evolution driven by the exchange of angular momentum between stars in the bar, and gas in the disc, acts as a feedback mechanism to regulate star formation in intermediate mass disc galaxies.Comment: 16 pages, 10 figures. In press at MNRAS. v2 contains corrections found in proof

An empirical investigation of the interaction manager-task using a human information processing approach. Available in 2 volumes.

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