Search Results for: La qualità dei nutraceutici e position paper per l’appropriato uso in medicina generale

Introduction
People with intellectual disability suffer from poorer health outcomes compared with the general population. Some of these inequalities are driven by systematic neglect of the healthcare system in responding to the needs of these people. This paper is a protocol for a mapping review that aims to systematically map the evidence base for the quality of healthcare for people with intellectual disability in Australia and similar universal health systems. It will use the Australian Health Performance Framework quality domains as a guide to define quality (effective, safe, appropriate, accessible, efficient/sustainable and continuity of care). The review aims to provide an overview of the existing evidence for quality in healthcare for people with intellectual disability, helping to steer future investments in improving the health and quality of life for people with intellectual disability.

Methods and analysis
A mapping review design has been chosen to address the broad aim and will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Scoping Reviews extension guidelines. Systematic searches of scientific databases and grey literature sources will be undertaken based on a search strategy developed in collaboration with academic librarians. Two reviewers will independently screen references against the inclusion/exclusion criteria. Visual/tabular summaries will then be produced alongside a descriptive overview. The mapping review has been registered with Open Science Framework (osf.io/7f8cy).

Ethics and dissemination
Formal ethical approval is not required as primary data will not be collected. This work is considered part of a larger stream of work by the National Centre of Excellence in Intellectual Disability and Health (NCoE) consisting of a consortium of expert organisations in intellectual disability and health. The NCoE will be engaged throughout the entirety of this review, including dissemination activities (presentations, reports, workshops and social media content).

Introduction
Chronic diseases place a large burden on health systems globally. While long-term planned care is essential for their management, episodes of deterioration are common. The emergence of rapid access to outpatient care has proliferated in response to increased resource pressures on acute health services. It is anticipated that these new models of care may prevent hospitalisations and reduce the burden on emergency departments. While some evidence supports the clinical effectiveness of these models, little is known about the core components and key attributes of these services. This paper outlines the protocol of a theory-driven, pragmatic process evaluation embedded within a new rapid-access outpatient service for chronic disease in South East Queensland, Australia.

Methods and analysis
This mixed-methods process evaluation will be conducted across three phases: (1) context assessment to identify programme characteristics and core components; (2) evaluation of key service processes and development of service improvement strategies and (3) sustainability assessment, with a focus on programme embedding and the resources associated with service evaluation. Each phase will be guided using implementation science frameworks and/or theory. Participants will include service consumers, service delivery staff, implementation leaders and decision-makers and wider system referrers. Professional stakeholders will be recruited through a direct invitation to participate (using purposeful sampling methods) and will be engaged in interviews at 1–3 data collection time points. Service consumers will be recruited through direct advertisement to participate in interviews. Administrative and clinical data collections will be retrospectively analysed with descriptive and inferential methods and triangulated with qualitative data to yield primary and secondary outcomes.

Ethics and dissemination
Ethical clearance has been obtained from the West Moreton Hospital and Health Service Human Research Ethics Committee. The planned dissemination of results will occur through conferences, abstracts and publications.

Trial registration number
Australia and New Zealand Clinical Trials Registry (ANZCTR Trial ID: ACTRN12624000757516).

Con uso tempestivo +50-70% di sopravvivenza ad arresto cardiaco

Objectives
Adherence to established reporting guidelines can improve clinical trial reporting standards, but attempts to improve adherence have produced mixed results. This exploratory study aimed to determine how accurate a large language model generative artificial intelligence system (AI-LLM) was for determining reporting guideline compliance in a sample of sports medicine clinical trial reports.

Design
This study was an exploratory retrospective data analysis. OpenAI GPT-4 and Meta Llama 2 AI-LLM were evaluated for their ability to determine reporting guideline adherence in a sample of sports medicine and exercise science clinical trial reports.

Setting
Academic research institution.

Participants
The study sample included 113 published sports medicine and exercise science clinical trial papers. For each paper, the GPT-4 Turbo and Llama 2 70B models were prompted to answer a series of nine reporting guideline questions about the text of the article. The GPT-4 Vision model was prompted to answer two additional reporting guideline questions about the participant flow diagram in a subset of articles. The dataset was randomly split (80/20) into a TRAIN and TEST dataset. Hyperparameter and fine-tuning were performed using the TRAIN dataset. The Llama 2 model was fine-tuned using the data from the GPT-4 Turbo analysis of the TRAIN dataset.

Primary and secondary outcome measures
The primary outcome was the F1-score, a measure of model performance on the TEST dataset. The secondary outcome was the model’s classification accuracy (%).

Results
Across all questions about the article text, the GPT-4 Turbo AI-LLM demonstrated acceptable performance (F1-score=0.89, accuracy (95% CI) = 90% (85% to 94%)). Accuracy for all reporting guidelines was >80%. The Llama 2 model accuracy was initially poor (F1-score=0.63, accuracy (95% CI) = 64% (57% to 71%)) and improved with fine-tuning (F1-score=0.84, accuracy (95% CI) = 83% (77% to 88%)). The GPT-4 Vision model accurately identified all participant flow diagrams (accuracy (95% CI) = 100% (89% to 100%)) but was less accurate at identifying when details were missing from the flow diagram (accuracy (95% CI) = 57% (39% to 73%)).

Conclusions
Both the GPT-4 and fine-tuned Llama 2 AI-LLMs showed promise as tools for assessing reporting guideline compliance. Next steps should include developing an efficient, open-source AI-LLM and exploring methods to improve model accuracy.

Introduction
Ageing is often accompanied by chronic diseases, multimorbidity and frailty, increasing the need for clinical and social care to support healthy ageing and manage these conditions. We are currently in the UN Decade of Ageing, and there is a growing focus on the need to prevent or delay some of these conditions through the ‘Healthy Ageing’ initiative of the WHO. However, there are limited data available to inform prioritisation of interventions, particularly for countries in sub-Saharan Africa.

Methods and analysis
This study will use a mixed-methods, stepwise approach to identify the current needs for older people in Rwanda, health system capacity and possible solutions to unmet need. First, we will conduct a household survey in the City of Kigali (predominantly urban) and Northern Province Burera district (predominantly rural) to determine the burden of multimorbidity, frailty, access to care, and experiences and responsiveness of care in older people. This work will be supplemented by secondary analysis of data from the Rwandan STEPwise approach to non-communicable disease risk factor surveillance (STEPs) survey of 2021. Second, we will conduct a health facility readiness assessment and healthcare provider survey to assess health system capacity and gaps to deliver effective primary care to older people in Rwanda. Third, to capture the voices of older people, we will explore the quality of healthcare as experienced by them using in-depth interviews. Fourth, we will synthesise data using mixed methods to understand barriers to access to quality of care among people of older ages based on a Three Delays framework (seeking, reaching and receiving quality healthcare). Finally, the project will culminate in a stakeholder workshop to ensure results are contextually appropriate and disseminated, and gaps identified are prioritised to design novel interventions to promote healthy ageing in Rwanda and the region.

Ethics and dissemination
The study has received ethics approval from the Rwanda National Ethics Committee, Northwestern University, USA, and the University of Birmingham, UK. This study will deliver impactful research by using multiple methodologies and working with in-country partners to develop a deep knowledge and understanding of healthcare systems experienced by older people in Rwanda. It will also provide a framework for sustainable healthy ageing research and policy engagement to benefit older adults living in Rwanda and inform similar work in low- and middle-income countries during this Decade of Healthy Ageing and beyond.

Objective
To assess the impact of introducing electronic decision support systems (EDSS)—electronic data entry implemented alongside existing paper-based antenatal care (ANC) records—on the completeness and agreement of ANC records.

Design
Two-phase cross-sectional (before and after) substudy of the mobile health integrated model of hypertension, diabetes and ANC (mIRA project) process evaluation.

Setting
Four rural districts in Bagmati Province, Nepal, in 19 primary healthcare facilities.

Participants
ANC records from pregnant women attending facilities before (n=136) and after (n=138) EDSS implementation.

Main outcome measures
For selected indicators in the ANC card and ANC register, we estimated the percentage completeness (any value recorded) and agreement (whether values matched) before and after EDSS implementation. We also reported the completeness of indicators in the EDSS and calculated the agreement between the ANC card and EDSS. 2 or Fisher’s exact test, as appropriate, was used to assess differences in completeness before and after implementation.

Results
Completeness of paper-based ANC records was high before implementation ( >90%) for all indicators, except tetanus vaccination (15% improvement in the completeness of tetanus vaccination date in paper-based ANC records (77.0%–96.4% for ANC cards and 81.9%–98.9% for ANC register). Agreement between the ANC card and ANC register increased slightly for all indicators after implementation, and the tetanus vaccination date showed the largest increase (38.2%–57.2%). Indicator completeness in the EDSS was low, ranging from 38.2% to 88.7%.

Conclusion
We found slight improvements in the completeness and agreement of paper-based ANC records following EDSS implementation. The lower percentage of completeness in the EDSS suggests that any large-scale implementation should consider how to integrate digital and paper-based records to decrease the data entry burden on ANC providers. However, the study’s small sample size limited the ability to examine variation in effects.

Objective
To assess the acknowledgement and mitigation of sex bias within studies using supervised machine learning (ML) for improving clinical outcomes in rheumatoid arthritis (RA).

Design
A systematic review of original studies published in English between 2018 and November 2023.

Data sources
PUBMED and EMBASE databases.

Study selection
Studies were selected based on their use of supervised ML in RA and their publication within the specified date range.

Data extraction and synthesis
Papers were scored on whether they reported, attempted to mitigate or successfully mitigated various types of bias: training data bias, test data bias, input variable bias, output variable bias and analysis bias. The quality of ML research in all papers was also assessed.

Results
Out of 52 papers included in the review, 51 had a female skew in their study participants. However, 42 papers did not acknowledge any potential sex bias. Only three papers assessed bias in model performance by sex disaggregating their results. Potential sex bias in input variables was acknowledged in one paper, while six papers commented on sex bias in their output variables, predominantly disease activity scores. No paper attempted to mitigate any type of sex bias.

Conclusions
The findings demonstrate the need for increased promotion of inclusive and equitable ML practices in healthcare to address unchecked sex bias in ML algorithms.

PROSPERO registration number
CRD42023431754.

Patologica la crescita delle università telematiche

L’idea di un gruppo di laureandi in Medicina all’Università delle Marche

Indagine Simeu: ‘Per tutti le aggressioni danneggiano la qualità delle cure’

Minore diffusione al Sud per la qualità di una molecola

Ingresso di giovani camici bianchi rafforza l’assistenza

Introduction
Mobile food markets may help to mitigate diet-related and weight-related inequities by bringing low-cost, nutritious food directly to underserved populations. By stocking foods to meet a range of dietary needs, full-service mobile markets may improve multiple aspects of diet, food security and fruit and vegetable procurement with a convenient one-stop shop.

Methods and analysis
This cluster randomised trial is evaluating the impact of a full-service mobile market, the Twin Cities Mobile Market (TCMM). The TCMM sells staple foods at affordable prices from a retrofitted bus that regularly visits communities experiencing low incomes. The trial’s primary outcome is participant diet quality. Secondary outcomes include intake of specific foods and nutrients, food security and servings of fruits and vegetables procured for the home.
Together with our partners, we enrolled four subsidised community housing sites in three waves (12 total sites), aimed to recruit 22 participants per site (N=264) and collected baseline data. Sites were then randomised to either receive the full-service TCMM intervention or serve as a waitlist control, and the full-service TCMM began implementing at intervention sites. Follow-up data collection is occurring at 6 months post-implementation. After follow-up data collection for each wave, the full-service TCMM intervention is being implemented at the waitlist control sites. Waves 1 and 2 are complete and Wave 3 is in progress.
At baseline and follow-up data collection, dietary quality and intake are being assessed through three, interviewer-administered, 24-hour dietary recalls, food insecurity is being assessed by the 18-item Food Security Screening Module and fruit and vegetable procurement is being measured by collecting one month of food procurement tracking forms.
We will use intent-to-treat analyses to determine if participant diet quality, food security and procurement of fruits and vegetables improve in the sites that received the full-service TCMM intervention relative to the participants in the waitlist control condition.

Ethics and dissemination
Trial procedures have been approved by the University of Minnesota Institutional Review Board. We plan to disseminate main outcomes in Grant Year 5 in both scientific and community spaces.

Trial registration number
ClinicalTrials.gov: NCT05672186.