Membership of the Royal College of Obstetricians and Gynaecologists (MRCOG) Clinical Skills, Evidence, Governance and Professional Practice Syllabus

Clinical Skills, Evidence, Governance and Professional Practice syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Clinical Skills, Evidence, Governance and Professional Practice in Membership of the Royal College of Obstetricians and Gynaecologists (MRCOG), not a summary of it.

1. 1

   Epidemiology and Statistics

   5 topics

   • Study design and hierarchy of evidence
     • RCTs, cohort, case-control and cross-sectional studies
     • Systematic reviews and meta-analysis
   • Descriptive and inferential statistics
     • Measures of central tendency and dispersion
     • Hypothesis testing, p-values and confidence intervals
   • Diagnostic test evaluation
     • Sensitivity, specificity and predictive values
     • ROC curves and likelihood ratios
   • Measures of risk and association
     • Relative risk, odds ratio and number needed to treat
   • Screening principles and Wilson-Jungner criteria
2. 2

   Clinical Governance, Risk and Quality

   5 topics

   • Audit cycle and quality improvement methodology
   • Risk management and incident reporting
     • Root cause analysis and serious incident review
     • Duty of candour
   • Clinical guidelines and protocol development
   • Maternal and perinatal mortality surveillance (MBRRACE)
   • Patient safety and human factors
3. 3

   Ethics, Law and Consent

   5 topics

   • Principles of medical ethics and confidentiality
   • Consent and capacity
     • Montgomery and informed consent
     • Mental Capacity Act and best interests
   • Legal frameworks in reproductive medicine
     • Abortion Act and HFEA regulation
   • Safeguarding children and vulnerable adults
   • End-of-life and fetal viability decisions
4. 4

   Communication and Professional Behaviour

   5 topics

   • Breaking bad news and shared decision-making
   • Communication with colleagues and handover (SBAR)
   • Managing complaints and difficult interactions
   • Teaching, supervision and appraisal
   • Leadership, teamworking and self-management
5. 5

   IT, Data and Information Governance

   3 topics

   • Data protection and electronic health records
   • Critical appraisal of literature and guidelines
   • Use of registries, datasets and coding

Clinical Skills, Evidence, Governance and Professional Practice flashcards for Membership of the Royal College of Obstetricians and Gynaecologists (MRCOG)

20 of 51 cards from the Clinical Skills, Evidence, Governance and Professional Practice deck — real questions with worked answers.

1. Rank the hierarchy of evidence from strongest to weakest study design.

   From strongest to weakest: systematic reviews/meta-analyses of RCTs > individual RCTs > cohort studies > case-control studies > cross-sectional studies > case series/case reports > expert opinion/anecdote.

2. What distinguishes a cohort study from a case-control study in terms of direction of enquiry?

   A cohort study is forward-looking: it starts with exposure and follows subjects to outcome (can be prospective or retrospective). A case-control study is backward-looking: it starts with outcome (cases vs controls) and looks back at exposure. Cohorts yield incidence/relative risk; case-control studies yield odds ratios and suit rare diseases.

3. Why is randomisation in an RCT important, and what does allocation concealment add?

   Randomisation balances known and unknown confounders between groups, minimising selection bias. Allocation concealment prevents those enrolling participants from knowing the next allocation, preventing manipulation of group assignment before randomisation.

4. Define incidence and prevalence and state their relationship.

   Incidence = number of NEW cases arising in a population at risk over a time period. Prevalence = total EXISTING cases (new + old) at a point or period. Relationship: $\text{Prevalence} \approx \text{Incidence} \times \text{Duration}$ of disease (for stable conditions).

5. When should you use the mean, median, or mode to describe central tendency?

   Mean for symmetrical/normally distributed continuous data. Median for skewed continuous or ordinal data (robust to outliers). Mode for nominal/categorical data or to identify the most frequent value.

6. Define the standard error of the mean and give its formula.

   The standard error (SEM) estimates the precision of the sample mean as an estimate of the population mean. $$SEM = \frac{s}{\sqrt{n}}$$ where $s$ is the sample standard deviation and $n$ the sample size.

7. What does a p-value represent, and what is the conventional significance threshold?

   The p-value is the probability of obtaining the observed result (or more extreme) if the null hypothesis were true. Conventionally $p < 0.05$ is taken as statistically significant. It does NOT give the probability that the null hypothesis is true, nor the effect size.

8. Distinguish Type I and Type II errors and relate them to power.

   Type I error ($\alpha$) = rejecting a true null hypothesis (false positive). Type II error ($\beta$) = failing to reject a false null hypothesis (false negative). Power $= 1 - \beta$ = probability of detecting a true effect; conventionally set at 80% or more.

9. How do you interpret a 95% confidence interval for a difference, and what does it imply if it crosses zero?

   A 95% CI is the range that, on repeated sampling, would contain the true population value 95% of the time. For a difference (e.g. mean difference), a CI crossing 0 implies the result is not statistically significant. For a ratio (RR/OR), crossing 1 implies non-significance.

10. Which statistical test compares means of two independent normally distributed groups, and which compares more than two?

    Two independent groups: unpaired (independent) Student's t-test. More than two groups: one-way ANOVA. Non-normal equivalents are the Mann-Whitney U test (two groups) and Kruskal-Wallis test (>2 groups).

11. Define sensitivity and specificity with their formulas.

    Sensitivity = ability to correctly identify those WITH disease: $$\text{Sensitivity} = \frac{TP}{TP + FN}$$ Specificity = ability to correctly identify those WITHOUT disease: $$\text{Specificity} = \frac{TN}{TN + FP}$$

12. Define positive and negative predictive value and state how prevalence affects them.

    $$PPV = \frac{TP}{TP + FP}, \quad NPV = \frac{TN}{TN + FN}$$ Predictive values depend on disease prevalence: as prevalence falls, PPV falls and NPV rises. Sensitivity and specificity are intrinsic to the test and prevalence-independent.

13. Define the likelihood ratios and give the positive likelihood ratio formula.

    Likelihood ratios express how much a test result changes the odds of disease. $$LR^{+} = \frac{\text{Sensitivity}}{1 - \text{Specificity}}, \quad LR^{-} = \frac{1 - \text{Sensitivity}}{\text{Specificity}}$$ $LR^{+} > 10$ and $LR^{-} < 0.1$ provide strong diagnostic evidence.

14. What does the area under a ROC curve represent?

    The ROC curve plots sensitivity (true positive rate) against $1 - \text{specificity}$ (false positive rate) across thresholds. The area under the curve (AUC) is the probability the test ranks a random diseased subject higher than a random non-diseased one. AUC = 0.5 is no better than chance; 1.0 is perfect.

15. Give the formulas for relative risk and odds ratio.

    $$RR = \frac{\text{incidence in exposed}}{\text{incidence in unexposed}} = \frac{a/(a+b)}{c/(c+d)}$$ $$OR = \frac{a \times d}{b \times c}$$ RR is used in cohort/RCTs; OR in case-control studies. OR approximates RR when the outcome is rare.

16. Define absolute risk reduction (ARR) and number needed to treat (NNT).

    $$ARR = \text{risk}_{\text{control}} - \text{risk}_{\text{treated}}$$ $$NNT = \frac{1}{ARR}$$ NNT = number of patients who must be treated to prevent one additional adverse outcome. A smaller NNT indicates a more effective intervention.

17. What is relative risk reduction (RRR) and why can it be misleading?

    $$RRR = \frac{\text{risk}_{\text{control}} - \text{risk}_{\text{treated}}}{\text{risk}_{\text{control}}} = \frac{ARR}{\text{risk}_{\text{control}}}$$ It can be misleading because a large RRR may correspond to a tiny absolute benefit when baseline risk is low; ARR/NNT give a truer picture of clinical impact.

18. List the Wilson and Jungner criteria for a screening programme.

    The condition must be an important health problem with a recognisable latent/early stage and understood natural history. A suitable, acceptable test must exist with an agreed policy on whom to treat. There must be an accepted, effective treatment, facilities for diagnosis/treatment, and the cost must be economically balanced; case-finding should be a continuing process.

19. Define lead-time bias and length-time bias in screening.

    Lead-time bias: screening detects disease earlier, so survival from diagnosis appears longer even if death is not delayed. Length-time bias: screening preferentially detects slow-growing, less aggressive disease (longer asymptomatic phase), overstating screening benefit.

20. Name the stages of the clinical audit cycle.

    1) Identify a problem/topic and set standards/criteria; 2) Collect data on current practice; 3) Compare performance against the standard; 4) Implement change; 5) Re-audit (close the loop) to confirm improvement. Audit is a continuous cyclical process.

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