CODEX Digest - 7.23.26

1) The role of oncology nurses in national breast and cervical cancer screening programs.

Brown C, McNally GA. Clin J Oncol Nurs. 2026;30(3):231-235.

Oncology nurses play a critical part in cancer prevention and early detection, especially for uninsured and underinsured women facing screening barriers. This article highlights how nurse involvement in large-scale screening programs boosts early diagnosis, survival, and quality of life, and how nurses educate patients on prevention and treatment and advocate for equitable healthcare access.

2) Measuring what matters: redefining undertriage using trauma team actions.

Fakhry SM, Shen Y, Orlando A, for the Geriatric Trauma Activation Criteria Research Study Group, Nashville, Tennessee. J Trauma Acute Care Surg. 2026;100(6):906-914.

Accurate trauma diagnosis depends on timely, high-level trauma response to reduce preventable deaths. This multicenter retrospective study compared two existing triage standards with a novel registry-based tool. This analysis of 158,696 records finds that the new method offers a streamlined, data-driven way to calculate undertriage rates and identify patients who may have benefited from full trauma team activation.

3) Intention to Treat--Season 2: The Race Equation.

Gotbaum R. N Engl J Med. 2026;394(18-24).

Biased clinical algorithms threaten diagnostic safety by producing disparate outcomes for Black patients. This podcast series examines how race-based medical algorithms can limit access to life-saving treatment, featuring physicians challenging these practices and the resistance they face. It also shows how difficult reform can be, even when clinicians recognize patient harm.

4) Drivers of unnecessary diagnostic imaging for uncomplicated low back pain among family physicians: a theory-informed qualitative study.

Jasaui Y, Mortazhejri S, Ruzycki SM, et al.  Fam Pract. 2026;43(3):cmag029.

Diagnostic imaging for uncomplicated, low back pain persists despite guidelines, fueling overdiagnosis and costs. This qualitative Canadian study reveals that family physicians’ decisions are driven by fear of missing serious pathology, not knowledge gaps. Key themes—beliefs, skills, resources, social influences, and reinforcement—highlight emotional and contextual factors. Effective interventions must address these anxieties directly, moving beyond simple educational recommendations to reduce unnecessary imaging.

5) Risk of first-ever stroke in the 90 days after hospital encounters: a case- crossover study.

Kalpakavadi S, Rehman S, Chappell K, et al. Cerebrovasc Dis. Epub 2026 Apr 16.

Hospital encounters before stroke are common but poorly understood. This Australian study of 1,240 first-ever stroke patients finds that patients experiencing hospital care in the prior 90 days were linked to more than double the stroke risk. Common diagnoses included nonspecific symptoms, circulatory disorders, and injuries. These results suggest targets for reducing misdiagnosis to improve primary stroke prevention.

6) Risk factors associated with delayed breast cancer diagnoses: what are we missing?

Kufer KS, Wellis LK, Harris A, et al. J Gen Intern Med. Epub 2026 Jun 10.

Timely diagnosis is essential for equitable breast cancer outcomes, yet causes of delay remain unclear. This retrospective cohort study examined health system, sociodemographic, and medical factors among 431 eligible patients; 22% experienced delayed diagnosis. Medicaid coverage and missed or cancelled appointments explained little variation, suggesting unmeasured influences play a larger role.

7) Digital storytelling interventions to promote cancer screening among Hispanic/Latino adults in primary care settings. 

Lohr AM, Sauver JS, O’Byrne JJ, et al. J Cancer Educ. 2026;41(3):553-561.

Cancer is a leading cause of death among Hispanic/Latino individuals, partly due to screening barriers. This study shares an innovation in which eight cancer survivors created short videos sharing their experiences to motivate others to get screened. Among 51 participants overdue for screening, all intended to get screened after viewing, and 45% did so within seven months; 89% of screened participants said the videos strongly influenced their decision. Barriers like time, access, and fear affected actual screening completion, but acceptability was high.

8) Screening for missed opportunities for diagnosis in the ED using etriggers and large language models.

Marks CM, Gibney S, Stenson B, et al. JAMA Netw Open. 2026;9(6):e2620939.

Emergency department (ED) electronic alerts aim to flag missed diagnoses but have limited accuracy. This retrospective study tested six commercial LLMs on 288 ED cases from two high-risk cohorts, and the models collectively found missed diagnoses in 13.5% with physician agreement on 81.9% of those triggers. Model performance varied by LLM and case type, suggesting that while LLMs may help screen for missed diagnoses, organizations should require careful model selection and build human review into the process to enhance reliability. 

9) Problem representation in the age of artificial intelligence: the state of a dying art?

McQuade CN, Dhaliwal G, Bonifacino E, et al. Diagnosis (Berl). Epub 2026 Apr 24.

Problem representation (PR), a clinician's changing conception of a patient's condition, is a foundational diagnostic reasoning skill. This commentary examines PR's history, educational role, and future as generative AI becomes more common. The authors argue that mastering PR remains essential for developing diagnostic expertise, even if AI alters how the skills supporting it are developed.

10) Symptom tracking in primary care: designing and testing an EHR-embedded safety-net system.

Salant T, Benneyan J, Zhong A, et al.  Jt Comm J Qual Patient Saf. Epub 2026 Jun 2.

Systematic tracking of concerning symptoms in primary care may help prevent missed or delayed serious diagnoses but remain underused. This single clinic pilot study tests an electronic health record–embedded symptom tracking system and finds the innovation improved the timeliness and completion of follow-up. Broader implementation will require redesigned clinical workflows to support routine, sustainable use.

11) Large language models exhibit greater diagnostic anchoring than physicians in a forced-choice vignette study.

Sheppert A, Shen C, Geissal E, et al. Int J Med Inform. 2026;219:106550.

Anchoring bias contributes to diagnostic error, but LLM susceptibility is unclear. In this study, eight LLMs and 25 physicians/residents evaluated cases with misleading patient-suggested diagnoses. LLMs ranked the incorrect diagnosis first 56% of the time, compared with 10–21% for clinicians, raising safety concerns for AI-supported medical decision-making.

12) Large reasoning models as thinking machines for medicine.

Zhou H-Y, Rodman A, Liu P, et al. Nat Biomed Eng. Epub 2026 Jun 23.

AI has shown promise in pattern recognition but struggles with causal reasoning needed for complex diagnostic choices. This commentary discusses how new reasoning models are working to mirror human problem-solving and support medical reasoning by integrating patient records, decision tools, feedback, and outcomes for active decision making. The authors posit that as flexible thinking partners, AI tools may help manage complex experiences, clarify difficult cases, free clinicians for patient care, and enhance pharmaceutical innovation.