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]


Background {#Sec1}
==========

Sentinel lymph node (SLN)-navigated gastrectomy is a less invasive alternative to conventional radical gastrectomy for early gastric cancer, which has been performed using the tracers in Far East. Tracers currently used for detection of SLNs of gastric cancer are dyes \[[@CR1], [@CR2]\] and radioisotopes \[[@CR3]\]. Radio-labeled SLNs are defined as hot nodules containing 10 times more radioactivity than surrounding tissue as measured by a gamma probe \[[@CR3]\], whereas blue-dyed SLN sampling is based on visual evaluation \[[@CR1], [@CR2]\], maybe yielding inconsistency between the observers. We reported that infrared light observation was more feasible than ordinary light observation for SLN-navigated gastrectomy with higher detection rates of SLNs when using indocyanine green (ICG) dye as the tracer \[[@CR4]\]. Compared with the radio-guided and combination methods with dual tracers, performance of SLN biopsy with the dye-only method is an inexpensive and practical technique that does not require involvement of the nuclear medicine department. However, some SLNs could be missed with dye-only method because of its nature of visual evaulation \[[@CR5]\]. The aim of this study was to quantitatively assess the visual evaluation of the infrared ICG image.

Patients and Methods {#Sec2}
====================

For the analysis, we enrolled 36 patients with appropriate digitization and storage of the removed lymph nodes among those who underwent SLN navigation surgery (SNNS) for early gastric cancer at our hospital. Open gastric resection was performed in 17 patients with a cT1N0 or cT2N0 tumor, and laparoscopic gastric resection was performed in 19 patients with a cT1N0 tumor. We examined a total of 563 lymph nodes retrieved from these patients. All patients provided written informed consent. This study was approved by the Institutional Review Board of the Jikei University Scholl of Medicine (Reference No.15-90) and conducted in accordance with the Good Clinical Practice guidelines and the Declaration of Helsinki.

Zero point five milliliters each of 5 mg/ml ICG (Diagnogreen^®^; Daiichi Sankyo Company, Limited), was injected intraoperatively into four quadrants of the submucosa around the tumor. Then, ordinary light observation followed by infrared light observation was performed by the infrared ray laparoscopic system (IRLS: Olympus Medical Systems Corp.) (Fig. [1](#Fig1){ref-type="fig"}). The use of infrared light allows clear observation of ICG-positive lymphatic ducts and the ICG-positive lymph nodes which are barely observable under ordinary light (Fig. [1](#Fig1){ref-type="fig"}). The lymphatic basin dissection method \[[@CR2]\] was applied for ICG-positive lymph nodes. The lymph nodes in the ICG-positive lymphatic basin were grouped subjectively into ICG-positive and -negative on the back table, and the ICG-positive lymph nodes were determined to be SLNs. Then, images of the lymph nodes were photographed under infrared light observation in the black box, created to avoid the influences of background contrast and ambient light (Fig. [2](#Fig2){ref-type="fig"}). Photoshop (Adobe Systems, San Jose, CA, USA) was used as the image-analyzing software. Elliptical marquee tool was set into the interested region of the harvested lymph node and the mean value of the histogram was calculated (Fig. [3](#Fig3){ref-type="fig"}). The mean value for the region of reference, defined as the background of the lymph node, was calculated in the same way. The ratio of the mean value of the region of interest to the mean value of the region of reference was calculated. Correlations between the calculated values of ICG intensity and ICG positivity/negativity were examined using the Student's *t*-test.Fig. 1Lymphatic ducts and lymph nodes, which are difficult to see under ordinary light (left), can be clearly visualized under infrared light (right)Fig. 2The black box. It is possible to photograph lymph nodes under the same conditions using this black box (left). A picture of lymph node in the black box under infrared light (right)Fig. 3lymph nodes photographed in the black box. ICG- positive and -negative lymph nodes are easy to distinguish **a** and **b**. In the lower line **c** and **d**, the values for the region of reference (yellow dot square) and the region of interest (yellow square) are calculated and quantified using Photoshop, and the ratio of the mean value of the region of interest to the mean value of the region of reference is 0.118 and 0.614, respectively

Results {#Sec3}
=======

In the comparison of ordinary light observation with infrared light observation, identification rates of the SNs were 78 % (28/36) vs 100 % (36/36), the mean ± SD numbers of SNs were 3.4 ± 3.7 (0--16) vs. 9.2 ± 5.9 (2--25) and the sensitivities were 20 % (1/5) vs. 100 % (5/5), showing better outcomes for infrared light observation (Table [1](#Tab1){ref-type="table"}). When the 563 lymph nodes were grouped into ICG -positive and ICG -negative under infrared light observation, 358 were ICG -positive and the other 205 were ICG -negative. The intensities (mean ± SD) were 0.323 ± 1.56 in the ICG-positive group and 0.639 ± 1.93 in the ICG-negative group, demonstrating a significant difference between the two groups (*P \<* 0.0001) (Fig. [4](#Fig4){ref-type="fig"}).Table 1Comparison of SNs' detection by ordinary light and infrared lightOrdinary lightInfrared ray lightICG-positive node\
detection28/36\
(78 %)36/36The number of\
ICG-positive nodes3.4 ± 3.7^\*^\
(0 to 16)^+^9.2 ± 5.9^\*^\
(2 to 25)^+^Lymph node\
metastasis sensitivity1/5\
(20 %)5/5\
(100 %)^\*^mean±SD,^+^(minimum to maximum)Fig. 4ICG intensity measured in ICG-positive and ICG-negative groups using the Student's *t*-test was a significant different

Discussion {#Sec4}
==========

In the advent of SNNS for gastric cancer, Miwa et al. reported that SLNs were detected in 31 of 35 (89 %) patients when they performed SNNSs using patent blue \[[@CR2]\]. Kitagawa et al. identified metastatic lymph nodes in 22 of 24 (92 %) patients who underwent SNNSs using the radioisotope method \[[@CR3]\]. Upon using ICG as the dye, Hiratsuka et al. reported being able to identify metastases to SNs in 9 of 10 (90 %) patients, but at the same time found metastases to lymph nodes other than SNs in 6 of 9 patients \[[@CR1]\]. Ichikura et al. identified metastatic lymph nodes in 11 of 13 (85 %) patients \[[@CR6]\]. We revealed that the identification rate and sensitivity of ICG-positive lymph nodes under ordinary light observation were lower than previous reports, while metastatic lymph nodes were identified in all 11 of 11 (100 %) patients by infrared light observation \[[@CR4]\]. Moreover; the metastatic lymph nodes were all ICG -positive. The present study also showed an acceptable SLN identification rate, and the sensitivity by the infrared ICG was significantly higher than that of ordinary light observation.

The infrared ray endoscopy system was established as a method for obtaining precise information on the submucosal blood vessels of the stomach, and when used in combination with ICG, which has a maximal absorption near 805 nm, it provides clearer visualization and enables observation to a depth of 3--5 mm \[[@CR7]\]. We have reported that infrared light observation was more useful than ordinary light observation in terms of both the SLN identification rate and the detectability of metastatic lymph nodes when ICG was used as the tracer \[[@CR4]\]. However, it is possible that some SLNs may be missed when dyes are used for SLN biopsy, because SLN detection is dependent on the ability of the observer to discriminate between ICG -positive and ICG --negative lymph nodes. We therefore attempted to quantitatively assess the visual evaluation of the ICG image. Initially, we tried to store digital images of samples that were mounted on gauze. Resultantly, the background was heterogeneous; photography under the same conditions was not possible. Thus, we created a black box, which enabled lymph nodes to be photographed under the same environmental conditions. A significant difference was found between the values of the ICG-positive and ICG-negative groups by photographing the lymph nodes under the same conditions using this black box and quantifying the digitized images. At presence, it seems difficult to quickly apply the method to clinical practice because of its nature of time-consuming ex vivo examination for quantitative assessment of the infrared ICG images. Radioisotope tracer method is more expensive, cumbersome, and demands specific logistical arrangements due to regulations regarding handling of radioactive material, therefore limiting the number of hospitals which is permitted to use radioactive tracers \[[@CR8]\]. Given considerable differences in the intensity (0.323 ± 1.56 vs 0.639 ± 1.93) between ICG-positive and ICG-negative lymph nodes, our study indicates that the current infrared ICG imaging is suitable for SLN biopsy not only in less invasive surgery for gastric cancer, but also for other tumors. In a prospective comparison of the infrared ICG and radioisotope technetium to detect SLNs in breast cancer, among 583 SLNs, 131 nodes (22.3 %) were fluorescent but cold (negative for radioisotope), and 6 (1 %) were not fluorescent (negative for ICG) but hot. Overall, ICG identified more SLNs per patient than did 99mTc (1.94 SLNs per patient vs 1.62 SLNs per patient \[[@CR9]\].

Conclusions {#Sec5}
===========

Our quantitative assessment of visual evaluation of the infrared ICG image of lymph nodes retrieved at surgery showed a significant difference in the intensity as measured by an imaging-software between ICG-positive and ICG-negative lymph nodes. These findings would erase the concern about the objectivity of the infrared ICG method.

ICG

:   infrared indocyanine green

IRLS

:   infrared ray laparoscopic system

SLNs

:   sentinel lymph nodes

SNNS

:   sentinel lymph node navigation surgery

We thank you the members of Department of Surgery, The Jikei University School of Medicine.

Funding {#FPar1}
=======

We received no support from any funding.

Availability of data and materials {#FPar2}
==================================

We clarified that the datasets supporting the conclusions of this article are included within the article and its additional file.

Authors' contributions {#FPar3}
======================

Naoto Takahashi, Hiroshi Nimura, <PRESIDIO_ANONYMIZED_PERSON> and Katsuhiko Yanaga conceived the study, analyzed the data, and drafted the manuscript. Shigeo Yamashita and Norio Mitsumori helped collect the data of ICG intensity and the clinical data. All authors read and approved the final manuscript.

Competing interests {#FPar4}
===================

Naoto Takahashi and co-authors declare that no financial competing interests in relation to this manuscript.

Ethics and consent {#FPar5}
==================

The study protocol and publication of the study results including the personal details were approved by the Institutional Review Board of the Jikei University Scholl of Medicine (Reference No.15-90). Written informed consent was obtained from all patients.