Description of study samples in each period with data on wheezing phenotypes.

±<p>Educated to school leaving certificate at 16 years (GCE level) or lower.</p

Adjusted associations of demographic, maternal, pregnancy and child characteristics with wheezing phenotypes in ALSPAC.

*<p>compared with never/infrequent wheezing (N = 5774) and using each child’s phenotype probability as weights.</p>±<p>Educated to school leaving certificate at 16 years (GCE level) or lower.</p>+<p>Chi-squared test across phenotypes.</p

Adjusted associations of postnatal characteristics with wheezing phenotypes in ALSPAC.

*<p>compared with never/infrequent wheezing (N = 4915) and using each child’s phenotype probability as weights.</p>+<p>Chi-squared test across phenotypes.</p

Adjusted associations of perinatal characteristics with wheezing phenotypes in ALSPAC.

*<p>compared with never/infrequent wheezing (N = 5642) and using each child’s phenotype probability as weights.</p>±<p>Not adjusted for each other but adjusted for preterm delivery (<37 weeks).</p>‡<p>Not adjusted for each other and not adjusted for birth weight/low birth weight (since birth weight does not influence gestational age).</p>+<p>Chi-squared test across phenotypes.</p

Presentation of Diagnostic Information to Doctors May Change Their Interpretation and Clinical Management: A Web-Based Randomised Controlled Trial

<div><p>Background</p><p>There is little evidence on how best to present diagnostic information to doctors and whether this makes any difference to clinical management. We undertook a randomised controlled trial to see if different data presentations altered clinicians’ decision to further investigate or treat a patient with a fictitious disorder (“Green syndrome”) and their ability to determine post-test probability.</p><p>Methods</p><p>We recruited doctors registered with the United Kingdom’s largest online network for medical doctors between 10 July and 6” November 2012. Participants were randomised to one of four arms: (a) text summary of sensitivity and specificity, (b) Fagan’s nomogram, (c) probability-modifying plot (PMP), (d) natural frequency tree (NFT). The main outcome measure was the decision whether to treat, not treat or undertake a brain biopsy on the hypothetical patient and the correct post-test probability. Secondary outcome measures included knowledge of diagnostic tests.</p><p>Results</p><p>917 participants attempted the survey and complete data were available from 874 (95.3%). Doctors randomized to the PMP and NFT arms were more likely to treat the patient than those randomized to the text-only arm. (ORs 1.49, 95% CI 1.02, 2.16) and 1.43, 95% CI 0.98, 2.08 respectively). More patients randomized to the PMP (87/218–39.9%) and NFT (73/207–35.3%) arms than the nomogram (50/194–25.8%) or text only (30/255–11.8%) arms reported the correct post-test probability (p <0.001). Younger age, postgraduate training and higher self-rated confidence all predicted better knowledge performance. Doctors with better knowledge were more likely to view an optional learning tutorial (OR per correct answer 1.18, 95% CI 1.06, 1.31).</p><p>Conclusions</p><p>Presenting diagnostic data using a probability-modifying plot or natural frequency tree influences the threshold for treatment and improves interpretation of tests results compared to text summary of sensitivity and specificity or Fagan’s nomogram.</p></div

Results by randomisation to method of presenting diagnostic test results.

<p>Results by randomisation to method of presenting diagnostic test results.</p

CONSORT flowchart.

<p>CONSORT flowchart.</p

Different modes of data presentation to help interpret the results of the index test (A) Fagan’s nomogram (B) Probability modifying plot (C) Natural frequency tree.

<p>Different modes of data presentation to help interpret the results of the index test (A) Fagan’s nomogram (B) Probability modifying plot (C) Natural frequency tree.</p

Random-effects meta-analysis of RORs associated with inadequate/unclear (versus adequate) sequence generation.

<p>The boxed section displays the average bias estimates, where available, from the seven meta-epidemiological studies contributing to the BRANDO 2012^a study (however only the BRANDO 2012^a ROR was included in our meta-analysis). The BRANDO 2012^a ROR is based on a multivariable analysis with adjustment for allocation concealment and double blinding [the corresponding univariable ROR is (95% CrI) 0.89 (0.82, 0.96)]. The BRANDO 2012^b ROR is based on a multivariable analysis with adjustment for allocation concealment and double blinding [the corresponding univariable ROR (95% CrI) is 0.89 (0.75, 1.05)]. The Unverzagt 2013^c ROR is based on a multivariable analysis with adjustment for allocation concealment, double blinding, attrition, selective outcome reporting, early stopping, pre-intervention, competing interests, baseline imbalance, switching interventions, sufficient follow-up, and single- versus multi-centre status [the corresponding univariable ROR (95% CI) is 0.98 (0.8, 1.21)]. The BRANDO 2012^d ROR is based on a multivariable analysis with adjustment for allocation concealment and double blinding [the corresponding univariable ROR (95% CrI) is 0.99 (0.84, 1.16)]. The BRANDO 2012^e ROR is based on a multivariable analysis with adjustment for allocation concealment and double blinding [the corresponding univariable ROR (95% CrI) is 0.83 (0.74, 0.94)].</p

Summary of characteristics of included meta-epidemiological studies.

<p>Summary of characteristics of included meta-epidemiological studies.</p