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1617 Background: Large language models (LLMs) are increasingly evaluated for oncology decision support, yet guideline concordance and hallucinations remain variable across cancer types and tasks. Retrieval-augmented generation (RAG) may improve safety by constraining the evidence base used during generation. Prior oncology LLM studies report discordance with guideline-based care, but few describe a reproducible health-system “vetting mechanism” that couples guideline-only retrieval with clinician-scored safety rubrics across multiple oncology domains. Methods: We assembled 316 de-identified oncology cases in a tumor-board template: medical oncology (breast, GI, GU, leukemia; n=216), gynecologic oncology (n=50), and neuro-oncology/CNS metastatic cancer (n=50). Each case was run on three systems: baseline GPT-4 (O1), NCCN-guideline anchored RAG (O2; guideline-constrained retrieval), and a literature-anchored system (OpenEvidence; O3). Oncologists independently scored outputs using a modified Generative Performance Score (mGPS; −1 to +1) combining guideline concordance with hallucination penalties; readability/rationality was scored on a 1–5 Likert scale. Inter-rater reliability was assessed (κ =.63). This workflow was designed as a pre-deployment model credentialing step for health-system governance (source policy → case bank → clinician-scored acceptance criteria). Results: Across all 316 cases (case-averaged), O2 achieved the highest mean total mGPS (0.75) compared with O1 (0.43) and O3 (0.59). In the medical oncology cohort (n=216), O2 outperformed O1 and O3 (mean mGPS 0.72 vs 0.35 and 0.54), with higher guideline concordance (0.85 vs 0.68 and 0.75) and lower hallucination penalties (−0.09 vs −0.25 and −0.19); readability was also highest for O2 (4.17 vs 3.22 and 3.86). In gynecologic oncology (n=50), O2 again performed best (mGPS 0.827) relative to O3 (0.697) and O1 (0.647). In the CNS cohort (n=50), mean composite mGPS favored O2 (0.82) over O3 (0.68) and O1 (0.55), while readability remained stable across models (3.55–3.73). Severe hallucination/unsafe recommendation rate: O1 [26%], O2 [5%], O3 [17%] (pre-specified definition; e.g., major fabricated citation or guideline-discordant harmful recommendation). Conclusions: In a 316-case, multi-domain clinician-scored evaluation, limiting retrieval to oncology guideline sources improved safety-aligned performance relative to unconstrained generation or literature-only anchoring. A guideline-constrained RAG configuration paired with clinician-scored rubrics and tail-risk reporting can serve as a practical health-system vetting mechanism for governance, re-validation, and change control of LLM decision support, including in resource-limited settings where reliable guideline-consistent recommendations are essential.

ObjectiveTo develop a new evidence‐based, pharmacologic treatment guideline for rheumatoid arthritis (RA).MethodsWe conducted systematic reviews to synthesize the evidence for the benefits and harms of various treatment options. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to rate the quality of evidence. We employed a group consensus process to grade the strength of recommendations (either strong or conditional). A strong recommendation indicates that clinicians are certain that the benefits of an intervention far outweigh the harms (or vice versa). A conditional recommendation denotes uncertainty over the balance of benefits and harms and/or more significant variability in patient values and preferences.ResultsThe guideline covers the use of traditional disease‐modifying antirheumatic drugs (DMARDs), biologic agents, tofacitinib, and glucocorticoids in early (<6 months) and established (≥6 months) RA. In addition, it provides recommendations on using a treat‐to‐target approach, tapering and discontinuing medications, and the use of biologic agents and DMARDs in patients with hepatitis, congestive heart failure, malignancy, and serious infections. The guideline addresses the use of vaccines in patients starting/receiving DMARDs or biologic agents, screening for tuberculosis in patients starting/receiving biologic agents or tofacitinib, and laboratory monitoring for traditional DMARDs. The guideline includes 74 recommendations: 23% are strong and 77% are conditional.ConclusionThis RA guideline should serve as a tool for clinicians and patients (our two target audiences) for pharmacologic treatment decisions in commonly encountered clinical situations. These recommendations are not prescriptive, and the treatment decisions should be made by physicians and patients through a shared decision‐making process taking into account patients’ values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies.

Objective To develop a new evidence‐based, pharmacologic treatment guideline for rheumatoid arthritis (RA). Methods We conducted systematic reviews to synthesize the evidence for the benefits and harms of various treatment options. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to rate the quality of evidence. We employed a group consensus process to grade the strength of recommendations (either strong or conditional). A strong recommendation indicates that clinicians are certain that the benefits of an intervention far outweigh the harms (or vice versa). A conditional recommendation denotes uncertainty over the balance of benefits and harms and/or more significant variability in patient values and preferences. Results The guideline covers the use of traditional disease‐modifying antirheumatic drugs (DMARDs), biologic agents, tofacitinib, and glucocorticoids in early (<6 months) and established (≥6 months) RA. In addition, it provides recommendations on using a treat‐to‐target approach, tapering and discontinuing medications, and the use of biologic agents and DMARDs in patients with hepatitis, congestive heart failure, malignancy, and serious infections. The guideline addresses the use of vaccines in patients starting/receiving DMARDs or biologic agents, screening for tuberculosis in patients starting/receiving biologic agents or tofacitinib, and laboratory monitoring for traditional DMARDs. The guideline includes 74 recommendations: 23% are strong and 77% are conditional. Conclusion This RA guideline should serve as a tool for clinicians and patients (our two target audiences) for pharmacologic treatment decisions in commonly encountered clinical situations. These recommendations are not prescriptive, and the treatment decisions should be made by physicians and patients through a shared decision‐making process taking into account patients’ values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies.

Aims and ObjectivesThis paper explores patients' perspectives on infection prevention and control.BackgroundHealthcare‐associated infections are the most frequent adverse event experienced by patients. Reduction strategies have predominantly addressed front‐line clinicians' practices; patients' roles have been less explored.DesignVideo‐reflexive ethnography.MethodsFieldwork undertaken at a large metropolitan hospital in Australia involved 300 hours of ethnographic observations, including 11 hours of video footage. This paper focuses on eight occasions, where video footage was shown back to patients in one‐on‐one reflexive sessions.FindingsViewing and discussing video footage of clinical care enabled patients to become articulate about infection risks, and to identify their own roles in reducing transmission. Barriers to detailed understandings of preventative practices and their roles included lack of conversation between patients and clinicians about infection prevention and control, and being ignored or contradicted when challenging perceived suboptimal practice. It became evident that to compensate for clinicians' lack of engagement around infection control, participants had developed a range of strategies, of variable effectiveness, to protect themselves and others. Finally, the reflexive process engendered closer scrutiny and a more critical attitude to infection control that increased patients' sense of agency.ConclusionThis study found that patients actively contribute to their own safety. Their success, however, depends on the quality of patient–provider relationships and conversations. Rather than treating patients as passive recipients of infection control practices, clinicians can support and engage with patients' contributions towards achieving safer care.Relevance to clinical practiceThis study suggests that if clinicians seek to reduce infection rates, they must start to consider patients as active contributors to infection control. Clinicians can engage patients in conversations about practices and pay attention to patient feedback about infection risk. This will broaden clinicians' understandings of infection control risks and behaviours, and assist them to support appropriate patient self‐care behaviour.