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]


Current management guidelines for influenza typically emphasize the prevention and treatment of uncomplicated seasonal disease \[[@ref1]\]. Nevertheless, even vaccinated patients may become seriously ill from influenza virus infection, and evidence to guide clinical care decisions for these patients is sparse. Data compiled from several recent studies suggest that the time has come to reconsider the approach to influenza treatment for patients who require hospitalization. Two questions must be answered: (1) is there a benefit to antiviral treatment of influenza in seriously ill patients, and (2) how should influenza be diagnosed in hospitalized patients?

Is There a Benefit to Treating Influenza in Seriously Ill Patients? {#sec1}
===================================================================

The only large randomized controlled trials that assessed the benefit of the treatment of influenza were trials of early therapy (\<48 h after symptom onset) with neuraminidase inhibitors (NAIs) in healthy adults and children. These trials demonstrated that therapy was associated with a significant reduction in the duration and severity of illness and a 40%--60% reduction in the percentage of patients who developed complications or required hospitalization \[[@ref2]\].

Similar reductions in complications and hospitalization associated with early therapy with oseltamivir have been identified in subsequent observational cohort studies of nursing home residents during influenza outbreaks, in 2 studies in which administrative databases were examined, and in cohort studies of immunocompromised patients \[[@ref5], [@ref8]\]. However, there is a difference between the early treatment of otherwise-healthy outpatients and the treatment of patients who require hospital admission. The question becomes, what data do we have on the impact of antiviral therapy for the treatment of influenza in patients requiring hospitalization?

There currently are 3 cohort studies that have examined the impact of treatment on patients with community-acquired illness severe enough to warrant hospitalization. In a cohort study by Falsey et al. \[[@ref11]\], viral testing was done systematically for patients admitted to Rochester General Hospital with underlying cardiopulmonary disease and respiratory tract infection, congestive heart failure, exacerbation of chronic lung disease, or acute respiratory viral illness during 4 winter seasons, from 1999 to 2003. Of the 193 patients with influenza, 53 received treatment with amantadine or rimantadine, and 15 received treatment with NAIs. In-hospital mortality among these patients was 6%. The investigators were unable to find an effect of treatment with antiviral drugs. However, it is important to recognize that the study was not powered to detect a clinically significant difference: an analysis based on a cohort of 200 patients has \<30% power to detect a 50% reduction in mortality. Furthermore, the authors indicated that it appeared that more severely ill patients were more likely to be treated with an antiviral drug, a bias that would decrease the probability of finding a treatment effect.

In a retrospective study of adult patients with influenza who were admitted to the Prince of Wales Hospital in Hong Kong during the 2004--2005 influenza season with fever and respiratory and systemic symptoms, a clinically and statistically significant reduction in length of hospital stay was associated with treatment with oseltamivir \[[@ref14]\]. In North America, less severely ill patients would have been screened from this cohort and sent home, but, in Hong Kong, most patients with fever and respiratory and systemic symptoms were admitted to a hospital regardless of disease severity and were screened for severe acute respiratory syndrome coronavirus and other respiratory viruses. Patients who had been symptomatic for \<2 days received empirical therapy with oseltamivir; patients who had been symptomatic for \>2 days received treatment at the discretion of their physician. In this study, a total of 356 patients were admitted to the hospital, and 257 received treatment with oseltamivir. Of those receiving treatment, 161 received treatment within 2 days of symptom onset. Patients who received treatment experienced a median reduction in their length of hospital stay of 2 days---an ∼30% reduction---relative to that of patients who did not receive treatment or who received treatment \>2 days after symptom onset (P\<.0001 ) \[[@ref14]\].

Further support for the use of specific antiviral therapy for hospitalized patients comes from data collected prospectively in a cohort study of patients in Toronto, which showed a surprisingly large reduction in mortality even when therapy was started \>48 h after symptom onset \[[@ref15]\]. This study correlated mortality with specific antiviral therapy over 2 influenza seasons (2004--2005 and 2005--2006) in Toronto. Of 327 adult patients with laboratory-confirmed influenza who were admitted to a hospital, 106 patients (32%) received treatment with oseltamivir. Overall in-hospital mortality in this cohort was 10.7%. Although the observation of a treatment effect was not anticipated, owing to the small sample size, patients who received treatment with oseltamivir had a risk of death (OR) of 0.21 (P=.03 ), corresponding to a point estimate of a 79% reduction in mortality, compared with patients who did not receive treatment. Given the limitations of this study\'s methodology, it is not possible to state unequivocally that oseltamivir treatment reduces mortality among patients admitted to a hospital with influenza. On the other hand, because of the established treatment effect in healthy outpatients, the apparent magnitude of the treatment effect in compromised and seriously ill patients, and the established safety of oseltamivir, a placebo-controlled trial to determine the efficacy of antiviral therapy for the treatment of severe influenza may no longer be ethically justifiable.

Of note, over the 3 seasons of surveillance in the Toronto Invasive Bacterial Diseases Network (TIBDN) study, about one-third of patients were hospitalized within 48 h of symptom onset. In addition, the treatment effect seen in this cohort was not different for patients treated \<48 h or \>48 h after symptom onset. These data confirm the findings of Ison et al. \[[@ref16]\], who demonstrated that hospitalized patients receiving treatment with rimantadine and zanamivir shed influenza virus for several days after hospital admission. Both the fact that one-third of patients present early and the fact that treatment may be effective when initiated \>48 h after symptom onset in hospitalized patients emphasize how important it is to improve our understanding of severe influenza illness. Another interesting finding from the TIBDN study is that, during the 2006--2007 influenza season, 2 of 21 patients admitted to an intensive care unit after out-of-hospital cardiac arrest tested positive for influenza \[[@ref15]\]. This raises a concern that influenza may trigger ventricular arrhythmias and sudden death and supports the results of a number of cohort studies suggesting that influenza vaccination is protective against sudden death.

This accumulating evidence for a treatment effect suggests that it is prudent to establish a policy of antiviral treatment for patients who are seriously ill with influenza. Such a policy will have a low risk of adverse events and a low risk of increasing selective pressure for the development of resistant strains of influenza virus. Oseltamivir has no proven serious adverse effects \[[@ref6], [@ref7], [@ref17]\]. The neurobehavioral adverse events reported primarily in Japanese adolescents may have been an effect of either influenza or treatment; only further study will resolve this question. Reassuringly, the rate of neurobehavioral adverse events reported to the US Food and Drug Administration by Japan is \<1 case per 100,000 prescriptions \[[@ref18]\]. For patients within the age range typically admitted to an intensive care unit, that rate is probably low enough to be of very limited clinical relevance. With regard to increasing selective pressure for the development of resistant virus strains, it is important to remember that NAIs are active against only influenza virus. If a patient treated with NAIs does not have influenza, no selective pressure is being applied, and antiviral resistance will not be increased in the patient population. This differs from the effect of empirical antibiotic therapy: every time an antibiotic is used, selective pressure is applied to the host\'s normal flora, whether or not the antibacterial is active against the pathogen being targeted.

Past influenza-management guidelines have not offered much guidance regarding antiviral treatment for patients admitted to a hospital. Nonetheless, recent guidelines suggest that antiviral therapy should be offered to patients with severe illness and to those who are most likely to develop complications and/or to die. For example, the Association of Medical Microbiology and Infectious Diseases Canada/Canadian Pediatric Society recommends antiviral treatment for individuals with severe illness and for those most likely to develop complications from influenza or to die prematurely as a result \[[@ref19]\]. The American Academy of Pediatrics recommends antiviral treatment for high-risk children and for other children with moderate to severe disease \[[@ref20]\]. Italian guidelines recommend prioritizing therapy for patients at risk of complications, ensuring that patients take the drug as early as possible, and using antiviral therapy only at the seasonal peak of influenza prevalence \[[@ref21]\].

How Should Influenza Be Diagnosed? {#sec2}
==================================

A decision to treat with antiviral therapy requires that physicians either rapidly make a reasonably definitive diagnosis of influenza or choose to treat empirically when the probability of influenza is above a certain threshold. The former is obviously preferable, when possible. Among adults, acute respiratory illness with fever and early cough has a positive predictive value for influenza of \>70% during influenza season \[[@ref22]\]. However, many adults, particularly those who are elderly or those who have a significant underlying illness, do not mount an adequate febrile response and may not present with early cough. Thus, clinical features alone cannot be used to diagnose influenza. Commonly available diagnostic tests for influenza are shown in [table 1](#tab1){ref-type="fig"} \[[@ref23]\]. Of the available laboratory tests, RT-PCR is preferred for its speed, sensitivity, and specificity but is not currently available to a majority of clinicians. In Toronto, most hospital laboratories provide EIA testing. On average, EIA sensitivity is 50%--70%, compared with that for viral culture, and specificity is ∼95%, depending on the laboratory and the test. However, the use of viral culture as the gold standard for sensitivity may be outdated. [Table 2](#tab2){ref-type="fig"} compares the sensitivity of viral culture to that of RT-PCR \[[@ref26]\]. The proportion of virus detected by culture in the different studies ranges from 50% to 90%, or ∼70% for purposes of estimation. Thus, actual rapid EIA sensitivity is not 60% but is closer to 60% of 70%, which is ∼42%. Other rapid tests with sensitivities ranging from 24% to 90%, relative to that for viral culture, share the same limitation \[[@ref24]\]. The lack of a rapid and sensitive clinical diagnostic test for influenza is problematic. Although a number of new and sensitive molecular techniques are being investigated, such tests will not become available for several years \[[@ref30]\].

![Laboratory diagnostic testing for influenza.](48-Supplement_1-S14-tbl001){#tab1}

![Likelihood of influenza virus detection by culture versus by PCR, with laboratory-confirmed influenza.](48-Supplement_1-S14-tbl002){#tab2}

Weighing the Benefits and Limitations of Diagnostic Testing versus Empirical Therapy {#sec3}
====================================================================================

Given the limitations of currently available diagnostic tests, empirical therapy is an option worth considering. Empirical therapy has the advantage of offering earlier treatment, which is likely to be more effective. It also may be the most cost-effective option, because laboratory testing actually may be more expensive than therapy (which costs ∼\$60 for a 5-day course). However, empirical therapy has the disadvantage of resulting in many more patients receiving treatment than actually have influenza. The prevalence of influenza in some recent patient cohorts is shown in [table 3](#tab3){ref-type="fig"} \[[@ref11], [@ref15], [@ref28], [@ref29]\]. These studies looked at data from different groups of patients, in different years and at different times of year. Seasonal and year-to-year variations in the underlying incidence of influenza explain much of the difference observed. Overall, these studies suggest that, during most influenza seasons, 10%--15% of adult patients with pneumonia and/or febrile respiratory illness are likely to have influenza virus infection \[[@ref29]\]. Thus, empirical therapy will result in 5--15 patients without influenza receiving treatment for every patient with influenza. This ratio is similar to that reported in recommendations for the use of empirical therapy for atypical bacterial infection in pneumonia and, thus, is worthy of consideration \[[@ref31], [@ref32]\].

![Prevalence of influenza in case series of patients admitted to a hospital.](48-Supplement_1-S14-tbl003){#tab3}

On the other hand, the testing of patients provides information to clinicians that enables more-directed therapy. Evidence from both pediatric and adult studies indicates that testing for influenza results in reduced use of antibiotics and possibly reduced use of some other diagnostic tests \[[@ref12], [@ref33], [@ref34]\]. Such observations suggest that the information is immediately useful to clinicians. However, false-positive test results occur and may mislead clinicians. The benefits and limitations of laboratory diagnostic testing versus empirical antiviral therapy for influenza are summarized in [table 4](#tab4){ref-type="fig"}.

![Benefits and limitations of diagnostic testing versus empirical antiviral therapy.](48-Supplement_1-S14-tbl004){#tab4}

Conclusion {#sec4}
==========

As a practical matter, each infectious disease specialist must weigh the uncertainties of diagnosis and the effects of treatment to determine the best option for each patient under his or her care. The accumulating evidence suggests that, for patients with acute cardiorespiratory illness requiring hospital admission during influenza season, consideration should be given to either prompt laboratory diagnostic testing and treatment for influenza virus-infected patients or empirical antiviral therapy for influenza. The best choice is made on a case-by-case basis and depends on the severity of illness in the patient being admitted (since earlier therapy for pneumonia is more effective), the probability of influenza virus infection in the individual patient, and the sensitivity of the rapid diagnostic tests available. It is hoped that the introduction of RT-PCR testing into hospital laboratories and the accumulating information from cohort studies and trials of antiviral therapy among severely ill patients with influenza will soon result in a better understanding of effective diagnosis and therapy and in improved outcomes for severely ill patients.

***Financial support***. BioCryst Pharmaceuticals, Inc., provided educational grant support to develop this article and the symposium on which it is based, "Antiviral Therapy for Influenza: Challenging the Status Quo" (San Diego), 4 October 2007.

***Supplement sponsorship***. This article was published as part of a supplement entitled "Antiviral Therapy for Influenza: Challenging the Status Quo," jointly sponsored by the Institute for Medical and Nursing Education and International Medical Press and supported by an educational grant from BioCryst Pharmaceuticals, Inc.

***Manuscript preparation***. Margery Tamas of International Medical Press (Atlanta) provided assistance in preparing and editing the manuscript.

***Potential conflicts of interest***. A.J.M. has served as a consultant to BioCryst Pharmaceuticals, Inc., has received funding for investigator-initiated research under contract with Hoffman-LaRoche and BioCryst Pharmaceuticals, Inc., and is a member of the speakers\' bureau for Gilead Pharmaceuticals.