Are sex and gender dimensions accounted for in NICE guidelines? A systematic review of 223 clinical guidelines
Bibliographic record
Abstract
Introduction: The importance of sex and gender in the diagnosis and management of health conditions is well established, but the extent to which sex and gender disaggregated evidence is integrated into UK clinical practice guidelines remains unknown. Recent reviews of clinical guidelines in Canada and Europe identified that limited and inconsistent attention was paid to sex and gender dimensions of health and illness. This study aimed to determine how the UK's National Institute of Clinical Excellence (NICE) clinical guidelines account for sex and gender. Methods: The study reviewed all NICE guidelines categorised as 'clinical guidelines' by NICE (223), excluding those solely linked to single-sex conditions (26). Reviewers evaluated whether they included information on sex and/or gender dimensions of disease risk, presentation, investigations and management. They also examined if sex and/or gender dimensions were considered outside of pregnancy, and how the gender of guideline committee chairs and members corresponded to how well sex and/or gender were accounted for. Results: Of 197 guidelines reviewed, 120 (61%) referenced sex and/or gender dimensions, with 81 (41%) referencing these dimensions outside of pregnancy and childbearing. A minority of guidelines mentioned sex and/or gender dimensions related to disease pathophysiology (2%), clinical presentation (9%), investigations (15%) and epidemiology (19%). 162 guidelines published details of their committee chairs, and 126 (76%) were men. Committees chaired by women tended to produce guidelines, which scored better for consideration of sex and gender. Conclusion: This study highlights key gaps in NICE guidelines which must be addressed through systematic, whole-sector progress to integrate sex and gender disaggregated research into clinical guidelines. As the single focal point responsible for guideline development in England and Wales, NICE has a unique opportunity to establish robust mechanisms to routinely embed this important evidence in guidelines. Multiple initiatives are recommended to identify relevant existing evidence across all clinical specialties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".