output files modded for processing

2 files changed, 81 insertions, 15 deletions

diff --git a/gnqa/paper2_eval/data/rag_out_1.json b/gnqa/paper2_eval/data/rag_out_1.json
index 1a6a3c2..d901c53 100644
--- a/gnqa/paper2_eval/data/rag_out_1.json
+++ b/gnqa/paper2_eval/data/rag_out_1.json
@@ -22,6 +22,61 @@
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Med. 359 (21) (Nov 20 2008)", "title": "2011 - Annotating individual human genomes.pdf", "version": "v0", "chunk_order": 158, "document_id": "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", "extraction_id": "80d78615-8424-5478-a01b-73e220bc0345", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, {"id": "11fc663d2-2833-51e7-ae6a-55b007a6e27c", "score": 1.0, "metadata": {"text": "insulin resistance, hypertension, and dyslipidemia (Obesity Education Initiative Expert Panel,  1998 ). Insulin resist-ance increases with age, and the incidence of diabetes rises sharply in the elderly (American Diabetes Association,  2010a ). \n In a few patients, genetic mutations appear to be associ-\nated with T2D (Roche  et al.  ,  2005 ; American Diabetes \nAssociation,  2010a ). For example, recent work using the DPP data has led to the identi  cation of 27 single nucle-", "title": "2012 - Systems Biology Approaches to Nutrition.pdf", "version": "v0", "chunk_order": 9596, "document_id": "6955478b-950d-5d29-b24c-3a5ca656f3ae", "extraction_id": "eb3de845-98db-505c-bb7f-c0f3259875fc", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, {"id": "bb55a705-7399-550e-8285-07c33654b909", "score": 1.0, "metadata": {"text": "19. Permutt MA, Wasson J, Cox N: Genetic epidemiology of diabe-\ntes.  J Clin Invest  2005, 115:1431-1439.\n20. Barroso I: Genetics of Type 2 diabetes.   Diabet Med  2005,\n22:517-535.\n21. Parikh H, Groop L: Candidate genes for type 2 diabetes.   Rev\nEndocr Metab Disord  2004, 5:151-176.\n22. Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN: Meta-\nanalysis of genetic association studies supports a contribu-\ntion of common variants to su sceptibility to common dis-\nease.   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Recent studies in\ndiabetes and cardiovascular disease suggest that the addition of\npreviously associated genetic risk loci in clinical risk models of those\ndiseases increases both discriminative and predictive accuracy, albeit\nonly marginally. Typically, the strongest predictors of disease onset\nare known clinical risk factors such as body mass index, age, or", "title": "2011 - Annotating individual human genomes.pdf", "version": "v0", "chunk_order": 66, "document_id": "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", "extraction_id": "c17c74fa-12a3-5072-bb48-c179055db14d", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, {"id": "ff801099-e737-57b1-91af-a4cea20adb87", "score": 1.0, "metadata": {"text": "and sex, weight for height greater than the 85th percentile, or weight greater than 120% of ideal for height) who also have any two of the following risk factors: family history of T2D (  rst -  or second - degree relative), maternal history \nof diabetes or GDM during child  s gestation, member of high - risk race/ethnicity group (Native American, African -(40   in) in men is considered an indicator of increased", "title": "2012 - Systems Biology Approaches to Nutrition.pdf", "version": "v0", "chunk_order": 9595, "document_id": "6955478b-950d-5d29-b24c-3a5ca656f3ae", "extraction_id": "eb3de845-98db-505c-bb7f-c0f3259875fc", "associatedQuery": "List any factors that help predict early onset of diabetes."}}], "kg_search_results": None},
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+        {"id": "bb2a67ec-135b-5d55-b33d-74b1dc085685", 
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+            "text": "type 2 diabetes risk, Diabetes 57 (11) (Nov 2008) 3129 3135.\n[103] Q. Lu, R.C. Elston, Using the optimal receiver operating characteristic curve to\ndesign a predictive genetic test, exempli ed with type 2 diabetes, Am. J. Hum.\nGenet. 82 (3) (Mar 2008) 641 651.\n[104] V. Lyssenko, A. Jonsson, P. Almgren, N. Pulizzi, B. Isomaa, T. Tuomi, G. Berglund, D.\nAltshuler, P. Nilsson, L. Groop, Clinical risk factors, DNA variants, and the\ndevelopment of type 2 diabetes, N. Engl. J. Med. 359 (21) (Nov 20 2008)", "title": "2011 - Annotating individual human genomes.pdf", 
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+        {"id": "11fc663d2-2833-51e7-ae6a-55b007a6e27c", "score": 1.0, "metadata": {"text": "insulin resistance, hypertension, and dyslipidemia (Obesity Education Initiative Expert Panel,  1998 ). Insulin resist-ance increases with age, and the incidence of diabetes rises sharply in the elderly (American Diabetes Association,  2010a ). \n In a few patients, genetic mutations appear to be associ-\nated with T2D (Roche  et al.  ,  2005 ; American Diabetes \nAssociation,  2010a ). For example, recent work using the DPP data has led to the identi  cation of 27 single nucle-", "title": "2012 - Systems Biology Approaches to Nutrition.pdf", "version": "v0", "chunk_order": 9596, "document_id": "6955478b-950d-5d29-b24c-3a5ca656f3ae", "extraction_id": "eb3de845-98db-505c-bb7f-c0f3259875fc", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, 
+        {"id": "bb55a705-7399-550e-8285-07c33654b909", "score": 1.0, "metadata": {"text": "19. Permutt MA, Wasson J, Cox N: Genetic epidemiology of diabe-\ntes.  J Clin Invest  2005, 115:1431-1439.\n20. Barroso I: Genetics of Type 2 diabetes.   Diabet Med  2005,\n22:517-535.\n21. Parikh H, Groop L: Candidate genes for type 2 diabetes.   Rev\nEndocr Metab Disord  2004, 5:151-176.\n22. Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN: Meta-\nanalysis of genetic association studies supports a contribu-\ntion of common variants to su sceptibility to common dis-\nease.   Nat Genet  2003, 33:177-182.", "title": "2006 - β2-adrenergic receptor and UCP3 variants modulate the relationship between age and type 2 diabetes mellitus.pdf", "version": "v0", "chunk_order": 86, "document_id": "0ea34c04-5d09-5a32-89a7-c3add179927a", "extraction_id": "acf69ed8-c7b0-5d9f-8005-de020c9cf699", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, 
+        {"id": "9bff43c0-fd12-572e-9996-24957edd17d2", "score": 1.0, "metadata": {"text": "insulin-dependent diabetes and schizophrenia, twin studies have demon-strated the existence of a significant genetic component (Kyvik et al., 1995;Plomin et al., 1994). Genetic factors also influence cardiovascular diseaseswhich occur in early or midlife, while for cardiovascular diseases occur-ring late in life there is little evidence of a genetic effect (Marenberg et al.,1994). Dementia has a very strong genetic component, not only withregard to early-onset monogenic types but also to late-onset", "title": "2001 - Demography in the age of genomics.pdf", "version": "v0", "chunk_order": 452, "document_id": "0f07fa43-feb6-5656-b7e7-b8faa86f5623", "extraction_id": "5f24a851-1de6-5b6e-8230-2da08806b01a", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, 
+        {"id": "2df84ccc-0d32-582e-bda6-9cd46bee5378", "score": 1.0, "metadata": {"text": "Three categories of increased risk of developing diabetes \nare currently recognized by the ADA: an FPG between 5.6 and 6.9   mmol/L (100 and 125   mg/dL), de  ned as having \nimpaired fasting glucose (IFG); a 2 - h OGTT between 7.8 and 11   mmol/L (140 and 199   mg/dL), de  ned as having \nimpaired glucose tolerance (IGT); an A1C between 5.7 and 6.4% with values between 6.0 and 6.4 considered very high risk (American Diabetes Association,  2010a ). \n It is estimated that approximately one - fourth of indi-", "title": "2012 - Systems Biology Approaches to Nutrition.pdf", "version": "v0", "chunk_order": 9590, "document_id": "6955478b-950d-5d29-b24c-3a5ca656f3ae", "extraction_id": "eb3de845-98db-505c-bb7f-c0f3259875fc", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, 
+        {"id": "ff30f187-d5c3-5d01-8026-0588a77e9f44", "score": 1.0, "metadata": {"text": "20 90 D20S451 0.006 10.7 5.4 (34) 8.42 5.4 (61) 0.30 (long duration)\nInteraction with age at diagnosis of diabetes\n19 1 D1S1665 0.004 37.4 8.1 (66) 41.2 8.3 (81) 0.23 (early onset)\n2 159 D2S1399/D2S1353 0.023 40.8 8.2 (53) 38.8 8.5 (94) 0.16 (late onset)\n3 135 D3S2460 0.036 37.7 8.6 (66) 41.0 8.0 (81) 0.16 (early onset)\n4 146 D4S1625 0.005 37.9 7.4 (52) 40.4 8.9 (95) 0.23 (early onset)\n65 5 D6S2427 0.024 38.0 7.8 (70) 40.9 8.8 (77) 0.18 (early onset)", "title": "2004 - A genome scan for diabetic nephropathy in African Americans.pdf", "version": "v0", "chunk_order": 64, "document_id": "5798fb6b-b3e6-57c4-9823-5428853dbfa1", "extraction_id": "01ec7832-8a80-5f5e-aa26-3648f572c4a1", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, 
+        {"id": "57dc1ee5-4252-52c3-92cb-e2ac36cdc4d6", "score": 1.0, "metadata": {"text": "prevention, even though there are great concerns about how such\ninformation will be perceived and utilized [68]. Recent studies in\ndiabetes and cardiovascular disease suggest that the addition of\npreviously associated genetic risk loci in clinical risk models of those\ndiseases increases both discriminative and predictive accuracy, albeit\nonly marginally. Typically, the strongest predictors of disease onset\nare known clinical risk factors such as body mass index, age, or", "title": "2011 - Annotating individual human genomes.pdf", "version": "v0", "chunk_order": 66, "document_id": "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", "extraction_id": "c17c74fa-12a3-5072-bb48-c179055db14d", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, 
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+            {
+                "content": "The factors that help predict early onset of diabetes include:\n\n1. **Genetic Factors**: Specific genetic mutations and polymorphisms have been associated with an increased risk of developing type 2 diabetes (T2D) [1], [2], [4], [5], [6], [8].\n2. **Clinical Risk Factors**: Known clinical risk factors such as body mass index (BMI), age, and sex are strong predictors of disease onset [9].\n3. **Family History**: A family history of T2D, particularly in first- or second-degree relatives, is a significant risk factor [10].\n4. **Maternal History**: A maternal history of diabetes or gestational diabetes mellitus (GDM) during the childs gestation increases the risk [10].\n5. **Ethnicity**: Being a member of a high-risk race/ethnicity group, such as Native American or African American, is associated with a higher risk [10].\n6. **Impaired Glucose Levels**: Impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and elevated A1C levels are indicators of increased risk [7].\n7. **Insulin Resistance and Related Conditions**: Conditions such as insulin resistance, hypertension, and dyslipidemia are linked to a higher risk of developing diabetes [4].\n\nThese factors collectively contribute to the prediction of early onset diabetes.", 
+                "role": "assistant"
+            }
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+    "created": 1723706623, 
+    "model": "gpt-4o-2024-05-13", 
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+}
diff --git a/gnqa/paper2_eval/data/rag_out_2.json b/gnqa/paper2_eval/data/rag_out_2.json
index bf54e5e..eb3bfcc 100644
--- a/gnqa/paper2_eval/data/rag_out_2.json
+++ b/gnqa/paper2_eval/data/rag_out_2.json
@@ -31,17 +31,28 @@
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-                "content": "The factors that help predict the early onset of diabetes include:\n\n1. **Genetic Factors**: Specific genetic mutations and polymorphisms have been associated with an increased risk of developing diabetes. For example, studies have identified multiple genetic variants that can predict the risk of type 2 diabetes [1], [2], [4], [5], [6].\n\n2. **Clinical Risk Factors**: Known clinical risk factors such as body mass index (BMI), age, and sex are strong predictors of disease onset [9].\n\n3. **Family History**: A family history of type 2 diabetes (T2D), particularly in first- or second-degree relatives, is a significant risk factor [10].\n\n4. **Maternal History of Diabetes**: A maternal history of diabetes or gestational diabetes mellitus (GDM) during the child's gestation increases the risk [10].\n\n5. **Race/Ethnicity**: Being a member of a high-risk race/ethnicity group, such as Native American or African American, is associated with a higher risk of developing diabetes [10].\n\n6. **Impaired Glucose Tolerance and Impaired Fasting Glucose**: Individuals with impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) are at increased risk of developing diabetes [7].\n\n7. **Insulin Resistance, Hypertension, and Dyslipidemia**: These conditions are associated with an increased risk of diabetes, particularly as they often occur together in metabolic syndrome [4].\n\n8. **Age at Diagnosis**: There is an interaction between genetic factors and the age at diagnosis, with certain genetic markers being more strongly associated with early-onset diabetes [8].\n\nThese factors collectively help in predicting the early onset of diabetes.", 
-                "role": "assistant"
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+                    "content": "The factors that help predict the early onset of diabetes include:\n\n1. **Genetic Factors**: Specific genetic mutations and polymorphisms have been associated with an increased risk of developing diabetes. For example, studies have identified multiple genetic variants that can predict the risk of type 2 diabetes [1], [2], [4], [5], [6].\n\n2. **Clinical Risk Factors**: Known clinical risk factors such as body mass index (BMI), age, and sex are strong predictors of disease onset [9].\n\n3. **Family History**: A family history of type 2 diabetes (T2D), particularly in first- or second-degree relatives, is a significant risk factor [10].\n\n4. **Maternal History of Diabetes**: A maternal history of diabetes or gestational diabetes mellitus (GDM) during the child's gestation increases the risk [10].\n\n5. **Race/Ethnicity**: Being a member of a high-risk race/ethnicity group, such as Native American or African American, is associated with a higher risk of developing diabetes [10].\n\n6. **Impaired Glucose Tolerance and Impaired Fasting Glucose**: Individuals with impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) are at increased risk of developing diabetes [7].\n\n7. **Insulin Resistance, Hypertension, and Dyslipidemia**: These conditions are associated with an increased risk of diabetes, particularly as they often occur together in metabolic syndrome [4].\n\n8. **Age at Diagnosis**: There is an interaction between genetic factors and the age at diagnosis, with certain genetic markers being more strongly associated with early-onset diabetes [8].\n\nThese factors collectively help in predicting the early onset of diabetes.", 
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