{ "status_code": 200, "request": { "fields": [ { "name": "country", "description": "Country or countries where the study population was recruited or data originates. Use standard country names.", "data_type_primary": "array-string", "examples": [ "United Kingdom", "United States", "Denmark", "International" ], "examples_mode": "guide", "depth": "minimal" }, { "name": "study_size", "description": "Total number of participants in the analysed cohort as a numeric value if explicitly stated; null if absent.", "data_type_primary": "number", "examples": [ "32091", "1263" ], "examples_mode": "guide", "depth": "minimal" }, { "name": "year_coverage", "description": "Decade bins covering the study data collection period. Select all applicable ranges.", "data_type_primary": "array-string", "examples": [ "1980-1989", "1990-1999", "2000-2009", "2010-2019", "2020-2029" ], "examples_mode": "enum", "depth": "minimal" }, { "name": "gender", "description": "Sex distribution of the study population.", "data_type_primary": "string", "examples": [ "Both", "Male", "Female", "Not Applicable" ], "examples_mode": "enum", "depth": "minimal" }, { "name": "age", "description": "Age categories of the study population. Select all applicable bins based on stated age range or mean/median age.", "data_type_primary": "array-string", "examples": [ "<45", "45-65", "65+" ], "examples_mode": "enum", "depth": "minimal" }, { "name": "publication_type", "description": "Study design or publication type explicitly stated.", "data_type_primary": "string", "examples": [ "Prospective cohort study", "Retrospective cohort study", "Meta-analysis", "Randomized controlled trial", "Cross-sectional study", "Systematic review" ], "examples_mode": "enum", "depth": "minimal" }, { "name": "model_type", "description": "Statistical or analytical modelling approaches used.", "data_type_primary": "array-string", "examples": [ "Cox proportional hazards model", "Logistic regression", "Random-effect meta-analysis", "Poisson regression" ], "examples_mode": "guide", "depth": "minimal" }, { "name": "data_source", "description": "Dataset, registry, cohort, or database from which study data were obtained.", "data_type_primary": "array-string", "examples": [ "UK Biobank", "NHANES", "Clinical Practice Research Datalink" ], "examples_mode": "guide", "depth": "minimal" }, { "name": "subpopulation", "description": "Specific subgroups or eligibility criteria analysed within the study.", "data_type_primary": "array-string", "examples": [ "General population", "Smokers", "COPD", "Lung Cancer", "Hypertensive subjects" ], "examples_mode": "guide", "depth": "minimal" }, { "name": "age_exact", "description": "Exact reported age summary statistic when available (e.g. mean, median with SD/IQR); null if absent.", "data_type_primary": "string", "examples": [ "59.7 (11.9)", "~55", "68.9 (12.8)" ], "examples_mode": "guide", "depth": "minimal" }, { "name": "follow_up_years", "description": "Duration of participant follow-up in predefined year ranges. Select all applicable bins.", "data_type_primary": "array-string", "examples": [ "<5", "5-9", "10-19", "20+" ], "examples_mode": "enum", "depth": "minimal" } ], "project_context": { "description": "Comprehensive genomic profiling/NGS in solid cancers.", "research_questions": [ "What is the incidence and prevalence of advanced cancers in Europe and other key jurisdictions?\n- Focus on France, Italy, Spain, Belgium, Switzerland, UK; also include data for rest of Europe and Brazil, Chile, Columbia, Mexico, Canada.", "What are EMA-approved and ESMO guideline-recommended biomarker-guided treatments in advanced cancer types?\n- Focus on the use of genomic testing in non-small-cell lung cancer, colorectal cancer, malignant melanoma, ovarian cancer and breast cancer, to reflect the different number and type of biomarkers associated with each type of malignant disease", "What are the types of precision medicine approaches in advanced cancers in Europe (that is, finding treatments that are effective for specific genomic biomarkers in a solid tumor)?\n- Targeted therapies for specific cancer types\n- Tissue-agnostic (pan-cancer) therapies\n- Multi-tumor type targeted therapies\n- Biomarker-based immunotherapy approaches\n- Emerging molecularly targeted therapies and ongoing clinical trials", "What are the unmet needs with current practice of genomic testing in advanced cancers in Europe?\n- What are the existing technologies for genomic testing?\n- What are the technology requirements for identification of specific types of genomic aberrations?\n- What are the key unmet needs in genomic testing workup approaches in current clinical practice?\n- What are the challenges faced by people trying to implement genomic testing for rate mutations?", "What is the clinical utility of comprehensive genome profiling (CGP)?\n- How far does CGP offer broader coverage of medically necessary biomarkers than more traditional methods of identifying mutations, such as polymerase chain reaction (PCR), chromosomal microarray (CMA) \u00c2\u00a0or small gene panel assays?\n- Is the accuracy of CGP higher than traditional approaches in detecting biomarkers?\n- Does the use of CGP improve patient outcomes, for example, do patients respond better to, or live longer with, therapies that target the patient's specific mutation?\n- Does the use of CGP mean that fewer biopsy samples are needed?\n- Does the use of CGP increase the number of patients who are eligible to be enrolled in clinical trials of new targeted therapies?", "Economic impact\n- What is the budget impact of adopting CGP compared with traditional genetic testing?\n- What is the cost-effectiveness of CGP?" ] }, "sample_records": [ { "ID": "20379", "Title": "Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal.", "Abstract": "OBJECTIVE: To map and assess prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease (COPD). DESIGN: Systematic review. DATA SOURCES: PubMed until November 2018 and hand searched references from eligible articles. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Studies developing, validating, or updating a prediction model in COPD patients and focusing on any potential clinical outcome. RESULTS: The systematic search yielded 228 eligible articles, describing the development of 408 prognostic models, the external validation of 38 models, and the validation of 20 prognostic models derived for diseases other than COPD. The 408 prognostic models were developed in three clinical settings: outpatients (n=239; 59%), patients admitted to hospital (n=155; 38%), and patients attending the emergency department (n=14; 3%). Among the 408 prognostic models, the most prevalent endpoints were mortality (n=209; 51%), risk for acute exacerbation of COPD (n=42; 10%), and risk for readmission after the index hospital admission (n=36; 9%). Overall, the most commonly used predictors were age (n=166; 41%), forced expiratory volume in one second (n=85; 21%), sex (n=74; 18%), body mass index (n=66; 16%), and smoking (n=65; 16%). Of the 408 prognostic models, 100 (25%) were internally validated and 91 (23%) examined the calibration of the developed model. For 286 (70%) models a model presentation was not available, and only 56 (14%) models were presented through the full equation. Model discrimination using the C statistic was available for 311 (76%) models. 38 models were externally validated, but in only 12 of these was the validation performed by a fully independent team. Only seven prognostic models with an overall low risk of bias according to PROBAST were identified. These models were ADO, B-AE-D, B-AE-D-C, extended ADO, updated ADO, updated BODE, and a model developed by Bertens et al. A meta-analysis of C statistics was performed for 12 prognostic models, and the summary estimates ranged from 0.611 to 0.769. CONCLUSIONS: This study constitutes a detailed mapping and assessment of the prognostic models for outcome prediction in COPD patients. The findings indicate several methodological pitfalls in their development and a low rate of external validation. Future research should focus on the improvement of existing models through update and external validation, as well as the assessment of the safety, clinical effectiveness, and cost effectiveness of the application of these prognostic models in clinical practice through impact studies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017069247." }, { "ID": "20380", "Title": "Mortality of workers certified by pneumoconiosis medical panels as having asbestosis.", "Abstract": "A mortality study has been carried out at the London, Cardiff, or Swansea Pneumoconiosis Medical Panels between 1952 and 1976 on people certified as suffering from asbestosis. The main analysis was of 665 men, 283 of whom had died. Of the deaths, 39% were from lung cancer, 9% mesothelioma, and 20% asbestosis. The observed mortality was compared with expectation based on the death rates for England and Wales. For all causes the observed number of deaths was 2.6 times expectation and for lung cancer 9.1 times expectation. After 10 years from first certification half of the men had died compared with an expectation of one in four. The excess death rates were apparent in the first year after certification and were still operating after 10 years on those who survived until then. The main factor influencing the mortality was the clinical state of the men at the time of certification, as indicated by the percentage disability awarded; the excess lung cancer rate and the mesothelioma and asbestosis rates all increased with percentage disability. Those awarded only 10% or 20% benefit were still at risk from all the three asbestos-related causes. For a man certified at age 55 it was estimated that his life expectation would be reduced by 3, 5, 8, or 12 years according to whether his rate of disablement benefit was 10%, 20%, 30% or 40% , or 50% or more respectively." }, { "ID": "20381", "Title": "Predicting COPD 1-year mortality using prognostic predictors routinely measured in primary care.", "Abstract": "BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major cause of mortality. Patients with advanced disease often have a poor quality of life, such that guidelines recommend providing palliative care in their last year of life. Uptake and use of palliative care in advanced COPD is low; difficulty in predicting 1-year mortality is thought to be a major contributing factor. METHODS: We identified two primary care COPD cohorts using UK electronic healthcare records (Clinical Practice Research Datalink). The first cohort was randomised equally into training and test sets. An external dataset was drawn from a second cohort. A risk model to predict mortality within 12 months was derived from the training set using backwards elimination Cox regression. The model was given the acronym BARC based on putative prognostic factors including body mass index and blood results (B), age (A), respiratory variables (airflow obstruction, exacerbations, smoking) (R) and comorbidities (C). The BARC index predictive performance was validated in the test set and external dataset by assessing calibration and discrimination. The observed and expected probabilities of death were assessed for increasing quartiles of mortality risk (very low risk, low risk, moderate risk, high risk). The BARC index was compared to the established index scores body mass index, obstructive, dyspnoea and exacerbations (BODEx), dyspnoea, obstruction, smoking and exacerbations (DOSE) and age, dyspnoea and obstruction (ADO). RESULTS: Fifty-four thousand nine hundred ninety patients were eligible from the first cohort and 4931 from the second cohort. Eighteen variables were included in the BARC, including age, airflow obstruction, body mass index, smoking, exacerbations and comorbidities. The risk model had acceptable predictive performance (test set: C-index = 0.79, 95% CI 0.78-0.81, D-statistic = 1.87, 95% CI 1.77-1.96, calibration slope = 0.95, 95% CI 0.9-0.99; external dataset: C-index = 0.67, 95% CI 0.65-0.7, D-statistic = 0.98, 95% CI 0.8-1.2, calibration slope = 0.54, 95% CI 0.45-0.64) and acceptable accuracy predicting the probability of death (probability of death in 1 year, n high-risk group, test set: expected = 0.31, observed = 0.30; external dataset: expected = 0.22, observed = 0.27). The BARC compared favourably to existing index scores that can also be applied without specialist respiratory variables (area under the curve: BARC = 0.78, 95% CI 0.76-0.79; BODEx = 0.48, 95% CI 0.45-0.51; DOSE = 0.60, 95% CI 0.57-0.61; ADO = 0.68, 95% CI 0.66-0.69, external dataset: BARC = 0.70, 95% CI 0.67-0.72; BODEx = 0.41, 95% CI 0.38-0.45; DOSE = 0.52, 95% CI 0.49-0.55; ADO = 0.57, 95% CI 0.54-0.60). CONCLUSION: The BARC index performed better than existing tools in predicting 1-year mortality. Critically, the risk score only requires routinely collected non-specialist information which, therefore, could help identify patients seen in primary care that may benefit from palliative care." } ] }, "response": { "suggestions": [ { "action": "modify", "field": { "name": "gender", "description": "Sex distribution of the study population. Specify the proportion or count for each group if available (e.g., % male, % female, or counts); otherwise, indicate predominant or exclusive gender.", "data_type_primary": "object", "data_type_secondary": "string", "examples": [ "{ \"male\": 54, \"female\": 46 }", "{ \"male\": \"100%\", \"female\": \"0%\" }", "Both", "Male", "Female", "Not Applicable" ], "examples_mode": "guide", "depth": "minimal", "extraction_difficulty": "" }, "rationale": "Capturing the actual distribution (percentages or counts) allows for more granular analysis and avoids loss of information. The current enum is too coarse for many studies.", "original_field_name": "gender", "target_field_name": null }, { "action": "add", "field": { "name": "cancer_type", "description": "Type(s) of cancer studied, using standard disease names (e.g., non-small-cell lung cancer, colorectal cancer, breast cancer).", "data_type_primary": "array-string", "data_type_secondary": "NA", "examples": [ "Non-small-cell lung cancer", "Colorectal cancer", "Malignant melanoma", "Ovarian cancer", "Breast cancer" ], "examples_mode": "guide", "depth": "minimal", "extraction_difficulty": "" }, "rationale": "Given the focus on specific cancer types in the research questions, this field is essential for filtering and subgroup analysis.", "original_field_name": null, "target_field_name": null }, { "action": "add", "field": { "name": "biomarkers_tested", "description": "List of genomic biomarkers or genetic alterations tested or reported in the study (e.g., EGFR, ALK, BRAF, BRCA1/2, MSI, TMB).", "data_type_primary": "array-string", "data_type_secondary": "NA", "examples": [ "EGFR", "ALK", "BRAF", "BRCA1", "MSI", "TMB" ], "examples_mode": "guide", "depth": "minimal", "extraction_difficulty": "" }, "rationale": "Biomarker information is central to the review's focus on precision medicine and genomic profiling.", "original_field_name": null, "target_field_name": null }, { "action": "add", "field": { "name": "genomic_testing_technology", "description": "Type(s) of genomic testing technology used (e.g., NGS, PCR, FISH, Sanger sequencing, chromosomal microarray, gene panel, whole exome sequencing, whole genome sequencing).", "data_type_primary": "array-string", "data_type_secondary": "NA", "examples": [ "NGS", "PCR", "FISH", "Gene panel", "Whole exome sequencing" ], "examples_mode": "guide", "depth": "minimal", "extraction_difficulty": "" }, "rationale": "Captures the method used for genomic profiling, which is crucial for comparing comprehensive genomic profiling (CGP) to traditional methods.", "original_field_name": null, "target_field_name": null }, { "action": "add", "field": { "name": "outcomes_reported", "description": "Key clinical or economic outcomes reported in the study (e.g., incidence, prevalence, overall survival, progression-free survival, response rate, cost-effectiveness, budget impact).", "data_type_primary": "array-string", "data_type_secondary": "NA", "examples": [ "Incidence", "Prevalence", "Overall survival", "Progression-free survival", "Response rate", "Cost-effectiveness" ], "examples_mode": "guide", "depth": "minimal", "extraction_difficulty": "" }, "rationale": "Explicitly capturing outcomes allows for more systematic mapping to the research questions and easier synthesis.", "original_field_name": null, "target_field_name": null }, { "action": "add", "field": { "name": "jurisdiction", "description": "Specific jurisdiction(s) (e.g., country, region, or continent) relevant to the study, especially if different from country of data origin or if the study covers multiple regulatory or healthcare systems.", "data_type_primary": "array-string", "data_type_secondary": "NA", "examples": [ "France", "Italy", "Europe", "Brazil", "Canada" ], "examples_mode": "guide", "depth": "minimal", "extraction_difficulty": "" }, "rationale": "Distinguishes between data origin and policy/regulatory context, which is important for questions about EMA/ESMO guidelines and regional practices.", "original_field_name": null, "target_field_name": null }, { "action": "add", "field": { "name": "treatment_types", "description": "Types of treatments or interventions studied or recommended (e.g., targeted therapy, immunotherapy, tissue-agnostic therapy, chemotherapy, hormone therapy).", "data_type_primary": "array-string", "data_type_secondary": "NA", "examples": [ "Targeted therapy", "Immunotherapy", "Tissue-agnostic therapy", "Chemotherapy" ], "examples_mode": "guide", "depth": "minimal", "extraction_difficulty": "" }, "rationale": 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