There is a growing market of new diagnostic tests being targeted directly to consumers, often before high-quality evidence about their effectiveness is available. This is causing issues in clinical practice as patients arrive at the NHS’s front door with results that have alarmed them.

The accuracy of these tests is unknown, developed in idealised populations then released to the general public, the positive predictive value of these tests plummet in the real world - but false positives require extensive further testing to rule out problems.

But advances in technology, with real potential, and commercial and societal incentives have aligned - these tests aren’t going to go away.

So how do we start grappling with the power and pitfalls of early diagnosis - define exactly how we should be evaluating and regulating this growing industry, and what changes will we need to put in place in our healthcare systems to make sure an early diagnosis is good diagnosis?

In this podcast;

Peter Donnelly, professor of public health medicine at the University of St Andrews

Bethany Jenkins, professor of health economics at the University of Leeds

Stuart Hogarth, researcher at the University of Cambridge

Andreas Halner, co-founder of Oxford Cancer Analytics

Alex Richter, director of clinical immunology services at the University of Birmingham

Carl Heneghan, director of the Centre for Evidence-Based Medicine at the University of Oxford.

This podcast was recorded at the Evidence Based Early Diagnosis Conference

Helen MacDonald and Juan Franco are back to examine the newest in evidence based medicine.

In this episode: gabapentinoids are medications routinely prescribed for treating epilepsy and nerve pain. But, they come with side effects; among them - dizziness, cognitive impairment, and a dent to motor coordination - as well as an increase in suicidal ideation for the patient. Seena Fazel is professor of forensic psychiatry at University of Oxford and joins Juan to discuss a new paper tracking the association of self-harm and gabapentinoids use in Sweden.

Meanwhile, GRADE is a fundamental tool for clinicians to assess the quality of evidence for medical interventions. But, the sheer number of recommendations in the GRADE series can be overwhelming. Gordon Guyatt is a distinguished professor of medicine at McMaster University in Canada, and an early figure in the rise of evidence based medicine. He joins Helen to reflect on the future of EBM, and how the new Core GRADE system can cut through the noise.

Reading list:

1. Associations between gabapentinoids and suicidal behaviour, unintentional overdoses, injuries, road traffic incidents, and violent crime: population based cohort study in Sweden

2. Why Core GRADE is needed: introduction to a new series in The BMJ

Helen MacDonald and Juan Franco are back, magnifying glasses in hand, to take a closer look at what’s happening in evidence based medicine.

In this episode: the NHS increasingly relies on physician associates to plug some of its leaks. But, what do academic studies show about the effectiveness of these newest healthcare team members? Trish Greenhalgh is professor of primary healthcare at the University of Oxford, and she tells us about her meta-study reviewing the evidence.

Meanwhile, chronic back pain can be debilitating. When it’s at its worst, patients are eager for treatments. But, what is the true effectiveness of spinal injections to alleviate pain? Jane Ballantyne is a retired professor of pain medicine. She discusses where interventions go wrong, and how new recommendations for pain management reflect the best evidence.

Finally, Helen and Juan face uncertainty. A new research methods and reporting paper published in the BMJ takes a critical look at clinical prediction models. What does the evidence tell clinicians about how to behave when outcomes can’t be exact?

Reading list:

1. Physician associates and anaesthetic associates in UK: rapid systematic review of recent UK based research.
2. Spinal interventions for chronic back pain
3. Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches