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]


###### Strengths and limitations of this study

-   Broad search strategy planned in order to identify a range of individual prognostic variables and multidimensional scores.

-   A focus on prognostic variables available in clinical practice (rather than, eg, genomics), such that the results will be meaningful to current practice.

-   The use of validated protocols and tools for data extraction, risk of bias assessment and reporting.

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-   Search restricted to patients with relatively stable disease, so we are unable to comment on prognostic variables of most use during or immediately after an exacerbation.

-   Despite rigorous use of protocols, there is a subjective element to any quality or risk of bias assessment.

Introduction {#s1}
============

Chronic obstructive pulmonary disease (COPD) is a complex, heterogeneous collection of conditions characterised by progressive irreversible expiratory airflow limitation. The prevalence of COPD is increasing globally and it is projected to be not only the third leading cause of death but also the seventh leading cause of disability-adjusted life years lost worldwide by 2030, representing an important public health challenge.[@R1] Patients with advanced COPD have significant morbidity, reduced quality of life and high mortality.[@R2]

Despite national[@R7] and international[@R8] guidelines recommending a palliative care approach in severe COPD, patients are unlikely to access specialist services or elements of 'general' palliative care such as advance care planning, promotion of physical and psychosocial health and family or carer support.[@R6] [@R9] Systematic identification of patients approaching the 'end-of-life' is a key recommendation of the end-of-life care strategy.[@R13] The unpredictable disease trajectory of COPD[@R14] makes this difficult. Policy literature uses the last year of life as the measure of those who are approaching death, and states that identification of this group is the first step in any palliative care process. However, there is no 'gold standard' method for predicting prognosis in COPD and no clear guidance on how to identify those in the last year of life. Easily measurable physiological parameters do not correlate well with mortality for individuals.[@R15] There are alternative methods for identifying patients who may benefit from specific services, such as needs-based assessment.[@R16] However, there are growing calls from patients, healthcare professionals and policymakers for better tools to aid prognostication which they see as a meaningful part of decision-making.[@R17] [@R18] Clinician predictions of survival are often inaccurate, and improvement in accuracy of prognostic tools has been identified as a research priority.[@R19]

A number of variables have been identified which are useful in making predictions about prognosis in COPD, in addition to the degree of airflow obstruction which was the historical way of staging the disease.[@R20] Scores that combine a number of variables have also been developed, in recognition of the fact that COPD is a multisystem disease. None of these scores are in widespread routine clinical use. This is partly because some variables used in these scores are not captured during routine care. The most well-validated prognostic score in COPD is the BODE (body mass index (BMI), FEV1% (forced expiratory volume in 1 s, % predicted for age and sex), MRC dyspnoea (Medical Research Council dyspnoea score) and 6MWT (6 min walk test)) index.[@R21] However, this has significant limitations as it requires a 6MWT, not routinely performed or recorded in primary care. A modification of the BODE score, the ADO[@R22] (age, MRC dyspnoea, FEV1%), has been developed to address this problem. These scores were developed in small cohorts, although efforts to modify and validate them in larger cohorts and different settings have demonstrated some external validity.[@R23]

A systematic review of multidimensional prognostic indices in COPD searched the literature up to September 2010.[@R26] This study will have some important differences: we will consider the strength and utility not only of composite scores but also of individual prognostic variables. The only outcome of interest will be mortality, and not exacerbation or hospitalisation; and our focus will specifically be on prediction of prognosis towards the end of life (\<12 months).

Methods and analysis {#s2}
====================

Aim {#s2a}
---

We aim to investigate known prognostic variables and scores that predict prognosis in COPD. We are specifically interested in those variables that contribute to risk assessment of patients in the community (ie, not hospitalised) for death within \<12 months. In developing this protocol, we referred to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (PRISMA-P) 2015 statement,[@R27] a guide for the standard reporting of systematic review protocols.

Inclusion criteria (participants, interventions, comparisons and outcomes) {#s2b}
--------------------------------------------------------------------------

Participants: Adults ≥35 years old with COPD as defined by GOLD.[@R1]

Interventions: We will include all randomised or quasi-randomised controlled trials, and prospective and retrospective longitudinal cohort and case--control studies which investigate prognostic variables, multivariate scores or models for COPD. We will include studies that describe the development, validation or impact assessment of prediction models.

Comparisons: Comparators and controls are less relevant in prognostic than intervention studies and may be absent in cohort studies.

Outcomes: The primary outcome of interest will be all-cause mortality ≤12 months following recording of prognostic variable or score.

Exclusion criteria {#s2c}
------------------

We will exclude the following literature: abstracts only (eg, conference paper), case studies and reviews; studies that are on patients with α-1-antitrypsin deficiency, or those who have undergone lung transplantation, lung volume reduction surgery or comparative interventional bronchoscopic procedures; studies in which the diagnostic criteria for COPD is unclear or does not meet GOLD criteria; studies in which people with COPD form a subgroup and no separate reporting is available; studies requiring hospitalisation to acquire or measure the prognostic variable or score; studies examining short-term prognosis following an exacerbation or hospitalisation; studies that investigate prognostic markers not easily available in clinical practice (eg, biomarkers in development, invasive investigations); and studies in which the only exposure is occupational or environmental (eg, air pollution).

Literature search {#s2d}
-----------------

We will search Ovid MEDLINE, EMBASE, the Cochrane database, Cochrane CENTRAL, DARE and CINAHL up to 30 April 2016. We will use medical subject heading and text words related to COPD, and broad strategies to identify prognostic studies and prognostic markers, focused on advanced disease and the end of life (see [figure 1](#BMJOPEN2016011677F1){ref-type="fig"}). Recognising potential limitations of electronic search strategies, we will supplement our search to identify potentially relevant studies from other sources, including reference lists of included studies, index-related articles on PubMed, and existing relevant reviews as well as Google Scholar search and ProQuest. Where necessary, authors will be contacted directly.

![Example Ovid search strategy, developed with the help of a medical librarian.](bmjopen2016011677f01){#BMJOPEN2016011677F1}

Selection of studies and extraction of data {#s2e}
-------------------------------------------

Two authors will scan the titles and abstracts of all literature retrieved by the initial search against inclusion and exclusion criteria and select articles for full-text review. All data will be downloaded to Zotero[@R28] for data management. Two authors will review the full-text articles to assess eligibility for inclusion in the report. Differences of opinion will be resolved by consensus, or by arbitration by a third reviewer. The authors will extract data independently using a prespecified data extraction tool. This will include details of the study setting, study design, population, diagnostic criteria for COPD (including cut-points for FEV1% predicted), method of measurement of each prognostic variable and outcome definition. In addition, it will include fields relevant to multivariate models based on the CHARMS checklist[@R29] such as modelling method, handling of predictors, method for selection of predictors, shrinkage of predictor weights, univariate and multivariate associations, model performance and evaluation. This will be piloted on the first five full-text reviews to ensure standardised use of the tool. The process of literature selection and reasons for exclusion will be fully documented and a PRISMA[@R27] flow diagram will be constructed.

Quality assessment {#s2f}
------------------

Two reviewers will assess quality and risk of bias of eligible studies based on prespecified domains. We will use an approach based on the Quality In Prognosis Studies (QUIPS) tool,[@R30] specifically designed for prognostic reviews. We will consider questions under six domains: study participation and attrition, prognostic factor measurement, outcome measurement, confounding measurement and account, analysis and other. Consensus will be reached by discussion, or by arbitration by a third reviewer.

Data synthesis {#s2g}
--------------

Owing to clinical and methodological heterogeneity in potentially included studies identified in the scoping review, it is not expected that formal meta-analysis will be possible. The planned method for evidence synthesis is therefore a narrative synthesis of all identified evidence. We will summarise the range of outcome predictors that have been studied to date. With regard to composite scores, we will assess not only the quality of model building but also the degree to which the scores have been externally validated and to what degree clinical utility and impact has been assessed.

We anticipate that many of the studies will be in restricted populations, such as trial populations, that may not represent the population of patients with COPD in the community. We will thus be cognisant of and comment on possible spectrum bias[@R31] and the implications for generalisability of findings. An assessment of the strength of evidence for each prognostic variable or score included will be formulated based on GRADE evidence profiles.[@R32]

Ethics and dissemination {#s3}
========================

No ethical approval is required, since this study is a synthesis of published studies. The results will be submitted for peer-reviewed publication and will be presented at national and international conferences.

The protocol has been registered in the PROSPERO database: CRD42016033866. Any amendments to the study protocol will be documented contemporaneously on the PROSPERO database site.

**Twitter:** Follow Laura-Jane Smith at [\@drlaurajane](http://twitter.com/drlaurajane)

**Contributors:** L-JES, JKQ, PS and LS made substantial contributions to the conception of the study. L-JES drafted the original protocol, with contributions from IA. It was reviewed by JKQ, PS and LS leading to revision for important intellectual content. All authors approved the final version for publication. L-JES acts as guarantor of the work.

**Funding:** This work was supported by The Wellcome Trust grant number WT107183, awarded to L-JES. The funder provided feedback on the overall research fellowship plan, but had no direct role in developing the systematic review protocol.

**Competing interests:** L-JES, IA and PS have nothing to disclose. JKQ reports grants from Medical Research Council, grants and personal fees from GlaxoSmithKline, grants from British Lung Foundation, personal fees from Astra Zeneca, outside the submitted work. LS reports grants from Wellcome Trust, grants from BHF, during the conduct of the study; grants from Wellcome Trust, grants from MRC, grants from NIHR, grants and personal fees from GSK, other from AstraZeneca, grants from European Union, outside the submitted work; and is a Trustee of the British Heart Foundation.

**Provenance and peer review:** Not commissioned; externally peer reviewed.