You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


1. Introduction
===============

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that manifests as symmetrical, progressive, and erosive arthritis.^\[[@R1]\]^ RA patients have an elevated incidence of reduced bone mass and osteoporosis due to active systemic inflammation and corticosteroid use. As a consequence, RA patients have a high risk of fracture, an outcome that can lead to impaired quality of life.^\[[@R2],[@R3]\]^

In recent years, research has focused on the identification of risk factors associated with osteoporotic fractures in RA patients, including RA disease duration, lack of physical activity, smoking, and glucocorticoid use.^\[[@R4]\]^ In particular, reduced bone mineral density (BMD) is a risk factor for osteoporosis in patients with RA, including those who do not receive oral glucocorticoids.^\[[@R5],[@R6]\]^ Currently, clinical diagnosis and assessment of osteoporosis is often made based on BMD evaluation using dual X-ray absorptiometry (DXA). BMD is commonly assessed by DXA in most RA patients with obvious clinical symptoms or secondary osteoporosis. However, other key risk factors for osteoporosis are not included when considering the probability of osteoporosis and osteoporotic fractures in this patient population.^\[[@R4]\]^

Since the Fracture Risk Assessment Tool (FRAX) was developed by the World Health Organization (WHO) in February 2008, it has been widely used for treatment decision making in osteoporosis. This Web-based algorithm has been incorporated into DXA reporting software to calculate the 10-year probability of major osteoporotic fracture of the vertebral column, hip, forearm, and humerus, and the 10-year probability of hip fracture in men and women based on easily obtained clinical risk factors and BMD of the femoral neck (optional). Algorithms that evaluate the probability of fractures and produce absolute risk estimates have been developed in various settings, and these are becoming widely available. FRAX is one of the most common FRAXs. FRAX incorporates demographic and clinical risk factors such as body mass index (BMI), and previous fracture to evaluate the 10-year risk of fractures and predict mortality.^\[[@R7],[@R8]\]^ FRAX has been used to assess the risk of fractures in postmenopausal women, and various country-specific FRAX models have been developed.^\[[@R9]\]^ However, to the authors' knowledge, FRAX has not been used to investigate risk factors or predict osteoporotic fracture risk in Chinese RA patients.

This study investigated the value of the Chinese-specific FRAX model as an FRAX in Chinese RA patients, and the role of osteoporotic-specific risk factors when evaluating the 10-year risk of osteoporotic fractures in this patient population.

2. Methods
==========

2.1. Study population
---------------------

This retrospective study included patients treated in our department for RA between 2012 and 2014. Patients who were ≥28 years old and diagnosed with RA were included. The diagnosis of RA was made in accordance with the 1987 American College of Rheumatology revised RA classification criteria. Exclusion criteria were: type I diabetes, osteogenesis imperfecta, untreated long-term hyperthyroidism, hypogonadism, premature menopause (≤45 years of age), chronic malnutrition, malabsorption, or chronic liver or kidney disease.

A control group consisted of healthy individuals who visited the outpatient department of our hospital medical center. Patients with serious liver and kidney diseases, metabolic bone disease, and those on medication were excluded. The Ethics Committee of our hospital approved this study, and all patients gave their written informed consent.

2.2. Data collection
--------------------

Demographic and clinical characteristics, including age, height, weight, BMI, smoking history, alcohol use, history of fragility fracture, family history of fragility fracture, BMD, lumbar vertebra (L1-L4) T-score, femoral neck T-score, DAS28, and oral glucocorticoid use (time and dosage), were recorded for all study participants.

Blood samples were collected from all study participants in the morning and analyzed for erythrocyte sedimentation rate (ESR), rheumatoid factor (RF), C-reactive protein (CRP), and anticyclic citrullinated peptide (anti-CCP).

2.3. Bone densitometry
----------------------

The BMD of L1-L4 and the femoral neck were measured utilizing DXA (GE Lunar Prodigy, Madison, WI). BMD (g/cm^2^) and T-scores were calculated. Outcomes were categorized according to WHO diagnostic criteria,^\[[@R5]\]^ where: T \> −1.0 is considered normal; T \> −2.5−≤ −1.0 is considered reduced bone mass; and T ≤ −2.5 is considered osteoporosis.

2.4. Chinese-specific FRAX model
--------------------------------

The Chinese mainland FRAX model (<http://www.shef.ac.uk/FRAX/>) was used in this study. The model uses clinical data to calculate the 10-year risk of fractures in Chinese individuals.

2.5. Statistical analysis
-------------------------

Data were analyzed by 2 independent investigators using SPSS v19.0 software (SPSS Inc., USA). All data are expressed as mean ± SD. Students *t* test or chi-square test was used to evaluate differences between groups. Correlations between variables were analyzed using Spearman correlation analysis. Receiver-operating characteristic curve and area under the curve were used to evaluate the 10-year risk of osteoporotic fractures in RA patients according to FRAX with or without BMD as a risk factor. *P* \< .05 was considered statistically significant.

3. Results
==========

3.1. Baseline characteristics
-----------------------------

This study included 168 RA patients, of which 23.2% (39) were male and 76.8% (129) were female. Average age was 54.3 ± 8.7 years (range, 28--80 years), and mean disease duration was 12.2 ± 9.5 years (range, 2--23 years). The control group included 168 participants, of which 27.38% (48) were male and 72.62% (122) were female. There were no significant differences in the baseline demographic and clinical characteristics between RA patients and controls (Table [1](#T1){ref-type="table"}).

###### 

Baseline demographic and clinical characteristics in RA patients and participants in the control group.

![](medi-96-e6677-g001)

3.2. BMD and T-score
--------------------

In RA patients, the incidence of osteoporosis was 41.1%; 43 cases were classified as normal, 56 cases were classified as reduced bone mass, and 69 cases were classified as osteoporosis.

In controls, the incidence of osteoporosis was 21.43%; 103 cases were classified as normal, 29 cases were classified as reduced bone mass, and 36 cases were classified as osteoporosis.

The incidence of osteoporosis was significantly higher in RA patients compared to controls (*P* \< .05). The BMD and T-score of L1--L4 and the femoral neck were significantly lower in RA patients compared to controls (*P* \< .05) (Table [2](#T2){ref-type="table"}). Analysis of patients stratified by gender showed a higher incidence of osteoporosis and a lower BMD and T-score of L1--L4 in male and female RA patients compared to controls (*P* \< .05, Table [3](#T3){ref-type="table"}).

###### 

BMD and T-score of L1--L4 and the femoral neck in RA patients and controls.

![](medi-96-e6677-g002)

###### 

BMD and T-score of L1--L4 and the femoral neck in male and female patients and controls.

![](medi-96-e6677-g003)

3.3. Ten-year risk of osteoporotic fractures
--------------------------------------------

The 10-year risk of hip fracture was (0.62 ± 0.11)% in RA patients and (0.17 ± 0.13)% in controls when BMD of the femoral neck was incorporated in FRAX. The 10-year risk of hip fracture was (0.56 ± 0.18)% in RA patients and (0.21 ± 0.61)% in controls when BMD of the femoral neck was not incorporated in FRAX. The 10-year risk of hip fracture in RA patients was significantly higher when BMD of the femoral neck was incorporated in FRAX compared with and without BMD (Table [4](#T4){ref-type="table"}). Similar findings were found in male and female RA patients (Table [5](#T5){ref-type="table"}).

###### 

The 10-year risk of osteoporotic fracture in RA patients and controls.

![](medi-96-e6677-g004)

###### 

The 10-year risk of osteoporotic fracture in male and female RA patients and controls.

![](medi-96-e6677-g005)

The 10-year risk of major osteoporotic fracture was (4.04 ± 0.83)% in RA patients and (2.29 ± 0.61)% in controls when BMD was incorporated in FRAX. The 10-year risk of major osteoporotic fracture was (3.86 ± 0.71)% in RA patients and (2.02 ± 0.66)% in controls when BMD was not incorporated in FRAX. The 10-year risk of major osteroporosis in RA patients was significantly higher when BMD of the femoral neck was incorporated in FRAX compared with and without BMD (Table [4](#T4){ref-type="table"}). Similar findings were found in male and female RA patients (Table [5](#T5){ref-type="table"}).

Receiver-operating characteristic curve analysis showed that the area under the curve for 10-year risk of major osteoporotic fracture was 70.2% in RA patients when BMD was incorporated in FRAX and 68.2% when BMD was not incorporated in FRAX (Fig. [1](#F1){ref-type="fig"}).

![Receiver-operating characteristic (ROC) curve for 10-year risk of major osteoporotic fracture.](medi-96-e6677-g006){#F1}

3.4. Risk factor analysis
-------------------------

The risk of hip fractures was negatively correlated with the BMD of L1--L4 (*r* = 0.734, *P* \< .05) and femoral neck (*r* = 0.708, *P* \< .01), and positively correlated with disease duration (*r* = 0.553, *P* \< .05), DAS28 (*r* = 0.578, *P* \< .05), and GC (*r* = 0.602, *P* \< .05). No obvious correlation was found between ESR, CRP, RF, anti-CCP, and hip fractures. Similar results were identified in osteoporotic fractures (Table [6](#T6){ref-type="table"}).

###### 

Risk factor analysis.

![](medi-96-e6677-g007)

4. Discussion
=============

This study evaluated the 10-year risk of osteoporotic fractures in Chinese RA patients and healthy individuals using the Chinese mainland FRAX model. Results showed that the BMD and T-score were lower and the 10-year risk of osteoporotic fractures was higher in Chinese RA patients compared with healthy individuals. BMD, disease duration, DAS28, and glucocorticoid use were important risk factors for hip and major osteoporotic fractures in Chinese RA patients, and the 10-year risk of osteoporotic fractures in Chinese RA patients was higher when BMD was incorporated in the FRAX model. In accordance with our findings, previous studies show that the rate of osteoporosis and the incidence of osteoporotic fractures are significantly increased in RA patients compared to the general population.^\[[@R3],[@R6],[@R10]--[@R14]\]^

The systemic effects of rheumatoid inflammation, immobility, nutritional problems, and weight loss are reported to increase bone resorption and contribute to osteoporosis in RA patients.^\[[@R15],[@R16]\]^ We used FRAX, which provides an easily applied and clinically relevant method for estimating the individual risk of fractures in RA patients. FRAX includes data routinely recorded in medical records, thus providing general practitioners the potential to use FRAX to evaluate the risk of fractures in RA patients in primary care. Currently, BMD is a major index for osteoporosis diagnosis. However, secondary osteoporosis in RA patients is considered a multifactorial disease. Therefore, BMD in combination with other risk factors should be used to predict the risk of fractures in patients with secondary osteoporosis. It is widely believed that treatments for osteoporosis are only effective in patients with reduced BMD. Thus, the use of FRAX without BMD to identify individuals is the subject of some debate and is a research topic of considerable interest. In fact, Kanis et al^\[[@R17]\]^ found that including BMD of the femoral neck in the FRAX model when calculating the probability of fracture facilitates the identification of patients who are most likely to require clinical intervention. In this study, we found that the Chinese mainland FRAX model that incorporated BMD of the femoral neck was superior for evaluating 10-year risk of osteoporotic fractures in Chinese patients with RA compared to the model without BMD. Similarly, Fraser et al^\[[@R18]\]^ reported that the Canadian FRAX model that incorporated BMD of the femoral neck was superior for predicting osteroporotic fracture in a Canadian population compared to the model without BMD; the authors concluded that the Canadian FRAX model that includes BMD of the femoral neck can be used to identify osteoporotic patients who require treatment. Furthermore, evidence suggests that the Spanish FRAX model that does not include BMD underestimates the risk of osteroporotic fracture in Spanish populations.^\[[@R19]\]^ Although several studies have reported that the use of FRAX for evaluating the risk of fracture in patients with inflammatory arthritis,^\[[@R20],[@R21]\]^ to our knowledge, this is the first study to use the Chinese-mainland FRAX model to evaluate fracture risk in Chinese patients with RA. In accordance with previous findings, our study showed that the risk of osteoporotic fractures in Chinese RA patients was highest with the FRAX model that included BMD.

The current study identified BMD, disease duration, DAS28, and glucocorticoid use as important risk factors for osteoporotic fractures in Chinese RA. These data are in accordance with previously published reports.^\[[@R21]--[@R25]\]^ In particular, 3 months of oral glucocorticoids and glucocorticoid ≥7.5 mg/day is associated with an increase in fracture risk. Long-term use of glucocorticoids can inhibit the intestinal absorption of calcium and promote calcium excretion, leading to lower serum calcium and bone resorption. Glucocorticoids can inhibit collagen synthesis and mineralization by osteoblasts and increase the activity of osteoclasts.^\[[@R26],[@R27]\]^

This study was associated with several limitations. First, the data were obtained retrospectively from a relative small patient population who were admitted to our department, which is located in a single-center. The evaluation of the small sample size and unmatched groups can introduce bias. Well-designed prospective studies are required to confirm our findings. Second, the follow-up duration was short. Third, the 10-year probability of fractures in RA patients was evaluated while pharmacological interventions were used. Bisphosphonates and selective estrogen receptor modulators reduce osteoporotic fractures. Therefore, the probability of fractures in RA patients receiving pharmacotherapies may differ from untreated patients.

5. Conclusion
=============

In summary, this study found a higher incidence of osteoporosis and reduced BMD in RA patients compared to healthy individuals. The FRAX model should include risk factors for osteoporotic fractures such as BMD, disease duration and activity, and glucocorticoid use when assessing the probability of fractures in RA patients.

Abbreviations: BMD = bone mineral density, DXA = dual X-ray absorptiometry, FRAX = Fracture Risk Assessment Tool.

The authors have no funding and conflicts of interest to disclose.