APERITIF – Automatic Patient Recruiting for Clinical Trials Based on HL7 FHIR

Clinical trials are carried out to prove the safety and effectiveness of new interventions and therapies. As diseases and their causes continue to become more specific, so do inclusion and exclusion criteria for trials. Patient recruitment has always been a challenge, but with medical progress, it becomes increasingly difficult to achieve the necessary number of cases. In Germany, the Medical Informatics Initiative is planning to use the central application and registration office to conduct feasibility analyses at an early stage and thus to identify suitable project partners. This approach aims to technically adapt/integrate the envisioned infrastructure in such a way that it can be used for trial case number estimation for the planning of multicenter clinical trials. We have developed a fully automated solution called APERITIF that can identify the number of eligible patients based on free-text eligibility criteria, taking into account the MII core data set and based on the FHIR standard. The evaluation showed a precision of 62.64 % for inclusion criteria and a precision of 66.45 % for exclusion criteria.

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A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

Applied Clinical Informatics ◽

10.1055/s-0041-1733846 ◽

2021 ◽

Vol 12 (04) ◽

pp. 816-825

Author(s):

Yingcheng Sun ◽

Alex Butler ◽

Ibrahim Diallo ◽

Jae Hyun Kim ◽

Casey Ta ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Electronic Health Records ◽

The United States ◽

Design Stage ◽

Common Data Model ◽

Free Text ◽

Eligibility Criteria ◽

Health Records ◽

Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

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2166

Journal of Clinical and Translational Science ◽

10.1017/cts.2017.59 ◽

2017 ◽

Vol 1 (S1) ◽

pp. 12-12

Author(s):

Jianyin Shao ◽

Ram Gouripeddi ◽

Julio C. Facelli

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Power Distribution ◽

Large Scale ◽

Search Space ◽

Free Text ◽

Eligibility Criteria ◽

Clinical Notes ◽

Semantic Concepts ◽

Mimic Iii

OBJECTIVES/SPECIFIC AIMS: This poster presents a detailed characterization of the distribution of semantic concepts used in the text describing eligibility criteria of clinical trials reported to ClincalTrials.gov and patient notes from MIMIC-III. The final goal of this study is to find a minimal set of semantic concepts that can describe clinical trials and patients for efficient computational matching of clinical trial descriptions to potential participants at large scale. METHODS/STUDY POPULATION: We downloaded the free text describing the eligibility criteria of all clinical trials reported to ClinicalTrials.gov as of July 28, 2015, ~195,000 trials and ~2,000,000 clinical notes from MIMIC-III. Using MetaMap 2014 we extracted UMLS concepts (CUIs) from the collected text. We calculated the frequency of presence of the semantic concepts in the texts describing the clinical trials eligibility criteria and patient notes. RESULTS/ANTICIPATED RESULTS: The results show a classical power distribution, Y=210X(−2.043), R2=0.9599, for clinical trial eligibility criteria and Y=513X(−2.684), R2=0.9477 for MIMIC patient notes, where Y represents the number of documents in which a concept appears and X is the cardinal order the concept ordered from more to less frequent. From this distribution, it is possible to realize that from the over, 100,000 concepts in UMLS, there are only ~60,000 and 50,000 concepts that appear in less than 10 clinical trial eligibility descriptions and MIMIC-III patient clinical notes, respectively. This indicates that it would be possible to describe clinical trials and patient notes with a relatively small number of concepts, making the search space for matching patients to clinical trials a relatively small sub-space of the overall UMLS search space. DISCUSSION/SIGNIFICANCE OF IMPACT: Our results showing that the concepts used to describe clinical trial eligibility criteria and patient clinical notes follow a power distribution can lead to tractable computational approaches to automatically match patients to clinical trials at large scale by considerably reducing the search space. While automatic patient matching is not the panacea for improving clinical trial recruitment, better low cost computational preselection processes can allow the limited human resources assigned to patient recruitment to be redirected to the most promising targets for recruitment.

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Prognostic impact of prevalent chronic lymphocytic leukemia stereotyped subsets: analysis within prospective clinical trials of the German CLL Study Group (GCLLSG)

Haematologica ◽

10.3324/haematol.2019.231027 ◽

2019 ◽

Vol 105 (11) ◽

pp. 2598-2607 ◽

Cited By ~ 8

Author(s):

Sonia Jaramillo ◽

Andreas Agathangelidis ◽

Christof Schneider ◽

Jasmin Bahlo ◽

Sandra Robrecht ◽

...

Keyword(s):

Clinical Trials ◽

Chronic Lymphocytic Leukemia ◽

Early Stage ◽

International Prognostic Index ◽

Prognostic Index ◽

Lymphocytic Leukemia ◽

Phase Iii ◽

Prognostic Impact ◽

Advanced Stage ◽

Multicenter Clinical Trials

Almost one-third of all patients with chronic lymphocytic leukemia (CLL) express stereotyped B cell receptor immunoglobulins (BcR IG) and can be assigned to distinct subsets, each with a particular BcR IG. The largest stereotyped subsets are #1, #2, #4 and #8, associated with specific clinicobiological characteristics and outcomes in retrospective studies. We assessed the associations and prognostic value of these BcR IG in prospective multicenter clinical trials reflective of two different clinical situations: i) early-stage patients (watch-and-wait arm of the CLL1 trial) (n=592); ii) patients in need of treatment, enrolled in 3 phase III trials (CLL8, CLL10, CLL11), treated with different chemo-immunotherapies (n=1861). Subset #1 was associated with del(11q), higher CLL international prognostic index (CLL-IPI) scores and similar clinical course to CLL with unmutated immunoglobulin heavy variable (IGHV) genes (U-CLL) in both early and advanced stage groups. IGHV-mutated (M-CLL) subset #2 cases had shorter time-to-first-treatment (TTFT) versus other M-CLL cases in the early-stage cohort (HR: 4.2, CI: 2-8.6, p<0.001), and shorter time-to-next-treatment (TTNT) in the advanced-stage cohort (HR: 2, CI: 1.2-3.3, p=0.005). M-CLL subset #4 was associated with lower CLL-IPI scores and younger age at diagnosis; in both cohorts, these patients showed a trend towards better outcomes versus other M-CLL. U-CLL subset #8 was associated with trisomy 12. Overall, this study shows that major stereotyped subsets have distinctive characteristics. For the first time in prospective multicenter clinical trials, subset # 2 appeared as an independent prognostic factor for earlier TTFT and TTNT and should be proposed for risk stratification of patients.

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Strategy for addressing research-site overlap in pragmatic clinical trials: lessons learned from the NIH-DOD-VA Pain Management Collaboratory (PMC)

Trials ◽

10.1186/s13063-020-04941-8 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Mary Geda ◽

◽

Steven Z. George ◽

Diana J. Burgess ◽

Dylan V. Scarton ◽

...

Keyword(s):

Clinical Trials ◽

Pain Management ◽

Research Effort ◽

Health Care Systems ◽

Lessons Learned ◽

Exclusion Criteria ◽

Eligibility Criteria ◽

Recruitment Methods ◽

Research Site ◽

Pragmatic Clinical Trials

Abstract Background The Pain Management Collaboratory (PMC) is a multi-site network of pragmatic clinical trials (PCTs) focused on nonpharmacological approaches to pain management, conducted in health care systems of the US Department of Defense (DoD) and Department of Veterans Affairs (VA) and co-funded by the National Institutes of Health (NIH). Concerns about potential research-site overlap prompted the PMC investigator community to consider strategies to avert this problem that could negatively affect recruitment and contaminate interventions and thus pose a threat to trial integrity. Methods We developed a two-step strategy to identify and remediate research-site overlap by obtaining detailed recruitment plans across all PMC PCTs that addressed eligibility criteria, recruitment methods, trial settings, and timeframes. The first, information-gathering phase consisted of a 2-month period for data collection from PIs, stakeholders, and ClinicalTrials.gov. The second, remediation phase consisted of a series of moderated conference calls over a 1-month time period to develop plans to address overlap. Remediation efforts focused on exclusion criteria and recruitment strategies, and they involved collaboration with sponsors and stakeholder groups such as the Military Treatment Facility Engagement Committee (MTFEC). The MTFEC is comprised of collaborating DoD and university-affiliated PIs, clinicians, and educators devoted to facilitating successful pragmatic trials in DoD settings. Results Of 61 recruitment sites for the 11 PMC PCTs, 17 (28%) overlapped. Four PCTs had five overlapping Military Treatment Facilities (MTFs), and eight PCTs had 12 overlapping VA Medical Centers (VAMCs). We developed three general strategies to avoid research-site overlap: (i) modify exclusion criteria, (ii) coordinate recruitment efforts, and/or (iii) replace or avoid any overlapping sites. Potential overlap from competing studies outside of the PMC was apparent at 26 sites, but we were not able to confirm them as true conflicts. Conclusion Proactive strategies can be used to resolve the issue of overlapping research sites in the PMC. These strategies, combined with open and impartial mediation approaches that include researchers, sponsors, and stakeholders, provide lessons learned from this large and complex pragmatic research effort.

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A multicentered population-based analysis of outcomes of patients with metastatic renal cell carcinoma (mRCC) who do not meet eligibility criteria for clinical trials.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.5_suppl.353 ◽

2012 ◽

Vol 30 (5_suppl) ◽

pp. 353-353 ◽

Cited By ~ 4

Author(s):

Daniel Yick Chin Heng ◽

Toni K. Choueiri ◽

Jae-Lyun Lee ◽

Lauren Christine Harshman ◽

Georg A. Bjarnason ◽

...

Keyword(s):

Clinical Trials ◽

Targeted Therapy ◽

Brain Metastases ◽

Clear Cell ◽

Karnofsky Performance Status ◽

Performance Status ◽

Progression Free Survival ◽

Exclusion Criteria ◽

Eligibility Criteria ◽

Number Of Patients

353 Background: Clinical trials have strict eligibility criteria to maintain internal validity. These criteria exclude many patients to whom the trial results are later applied to in clinical practice. Patients that do not meet eligibility criteria are poorly characterized. Methods: mRCC patients treated with VEGF targeted therapy were retrospectively deemed ineligible for clinical trials (according to commonly used inclusion/exclusion criteria) if they had a Karnofsky Performance Status (KPS) < 70%, brain metastases, non-clear cell histology, hemoglobin ≤ 9 g/dL, creatinine > 2x the upper limit of normal, platelet count of < 100x103/uL, neutrophil count < 1500/mm3 or corrected calcium ≤ 12 mg/dL. Results: 894/2076 (43%) patients were deemed ineligible for clinical trials by the above criteria. Between ineligible versus eligible patients, the response rate, median progression free survival (PFS) and median overall survival of first-line targeted therapy were 21% vs 29%, 5.2 vs 8.8 months and 14.5 vs 28.8 months (all p < 0.0001), respectively. Second-line PFS (if applicable) was 3.2 months in the trial ineligible vs 4.4 months in the trial eligible patients (p = 0.0074). When adjusted by the Heng et al prognostic categories, the hazard ratio for death between trial ineligible vs trial eligible patients was 1.621 (95% CI = 1.431–1.836, p < 0.0001). If only KPS, brain metastases and non-clear cell histology were used as exclusion criteria, 672 (32%) patients were excluded and the results were similar. Conclusions: The number of patients that are ineligible for clinical trials is high and their outcomes are inferior. Designing more inclusive clinical trials for this “ineligible” patient population are needed. [Table: see text]

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AI4CoV: Matching COVID-19 Patients to Treatment Options Using Artificial Intelligence

10.1101/2020.11.29.20240614 ◽

2020 ◽

Author(s):

Andrew I. Hsu ◽

Amber S. Yeh ◽

Shao-Lang Chen ◽

Jerry J. Yeh ◽

DongQing Lv ◽

...

Keyword(s):

Clinical Trials ◽

Language Processing ◽

Cross Validation ◽

Early Stage ◽

Treatment Options ◽

Clinical Manifestations ◽

Ground Truth ◽

English Text ◽

Eligibility Criteria ◽

Clinical Notes

AbstractWe developed AI4CoV, a novel AI system to match thousands of COVID-19 clinical trials to patients based on each patient’s eligibility to clinical trials in order to help physicians select treatment options for patients. AI4CoV leveraged Natural Language Processing (NLP) and Machine Learning to parse through eligibility criteria of trials and patients’ clinical manifestations in their clinical notes, both presented in English text, to accomplish 92.76% AUROC on a cross-validation test with 3,156 patient-trial pairs labeled with ground truth of suitability. Our retrospective multiple-site review shows that according to AI4CoV, severe patients of COVID-19 generally have less treatment options suitable for them than mild and moderate patients and that suitable and unsuitable treatment options are different for each patient. Our results show that the general approach of AI4CoV is useful during the early stage of a pandemic when the best treatments are still unknown.

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119 A Descriptive Epidemiological Study of Clinical Trials, Comparing Trials Targeted At Older People to Adults of All Ages

Age and Ageing ◽

10.1093/ageing/afab030.80 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

pp. i12-i42

Author(s):

J P Renton ◽

D McAllister

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Adverse Events ◽

Older People ◽

Exclusion Criterion ◽

Trial Participation ◽

Exclusion Criteria ◽

Eligibility Criteria ◽

Serious Adverse Events ◽

Icd 10

Abstract Introduction Fewer clinical trials are carried out in older people. It is unclear how representative and applicable clinical trials carried out exclusively in older people are. We compared clinical trials recruiting older people exclusively “older trials” to those recruiting adults of all ages “all age trials”, using anti-hypertensives acting on the renin-angiotensin aldosterone system (RAAS drugs) as an exemplar. Method We searched the US clinical trials register 1, to identify all trials carried out exclusively in those aged over 60. From these we selected trials of RAAS drugs. These were matched in a 2:1 ratio to trials carried out in adults of all ages. Data regarding baseline characteristics, adverse events and eligibility criteria were collected from clinical trial reports and clinicaltrials.gov. Estimated associations were calculated for age, sex and adverse events. Eligibility criteria were described and ICD- 10 coded, as appropriate. Results 71 clinical trials were carried out exclusively in older people.13 related to RAAS drugs. Participants in “Older trials” had higher mean age (73.1 and 55.9 respectively), mean difference 16.17 (CI 15.31–17.02). Older trials had fewer male participants. Participants in older trials had lower mean body mass index (BMI). A higher rate of participants in older trials experienced serious adverse events. (2.07, CI 1.55–2.75.) Few older trials had upper age limits (23.1% V 27% all age trials). All trials had exclusion criteria in multiple ICD blocks. Concurrent medications were a more common exclusion criterion in older trials (61.5% v 40.9%). Conclusions Clinical trials carried out exclusively in older people are representative in terms of age, serious adverse events and eligibility. Although there are multiple exclusion criteria for clinical trial participation in both groups, this is not prohibitive. This supports carrying out more trials exclusively in older people. References 1. NIH, US national library of medicine.ClinicalTrials.gov Available at https://clinicaltrials.gov/

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P166 Multimorbidity in SLE, a barrier to clinical trial eligibility: results from the BILAG-BR

Rheumatology ◽

10.1093/rheumatology/keab247.162 ◽

2021 ◽

Vol 60 (Supplement_1) ◽

Author(s):

Sarah Dyball ◽

Sophie Collinson ◽

Emily Sutton ◽

Eoghan McCarthy ◽

Ben Parker ◽

...

Keyword(s):

Clinical Trials ◽

Real World ◽

Phase Iii ◽

Research Support ◽

Exclusion Criteria ◽

Eligibility Criteria ◽

Chi Squared ◽

The Mean ◽

Double Blinded ◽

Age And Sex

Abstract Background/Aims Stringent inclusion and exclusion criteria are employed in SLE clinical trials. Organ dysfunction and co-morbidities are common exclusion criteria which may affect how representative trials are of real-world SLE populations. We aimed to apply published trial eligibility criteria to patients with SLE in a large national register. Methods A literature review of all major published double-blinded randomised phase III trials in non-renal SLE was performed. Common inclusion and exclusion criteria were applied to all patients recruited to the BILAG-Biologics Register (BILAG-BR), a large UK-wide register of SLE patients. Data on comorbidities for all patients registered was collected. The mean (SD) number of co-morbidities was calculated. Patients were then classified as being eligible or ineligible. Groups were compared initially using a chi-squared or Wilcoxon rank-sum test and logistic regression model was used to test the age and sex adjusted association between trial eligibility and comorbidities. Results Common inclusion and exclusion criteria were identified from 12 published trials. When applied to the 837 patients recruited to BILAG-BR, 562 (67%) patients would not be eligible for inclusion in these trials. Ineligible patients had a shorter disease duration (2.9 vs. 5.1 years, p < 0.01), but were similar in age (P = 1.0), sex (P = 0.7) and ethnicity (p = 0.5) to those who were eligible. Of eligible patients, 128 (53%) had 1 or more comorbidities compared with 340 (60%) who were ineligible (p = 0.05). The mean (SD) number of comorbidities was 0.9 (1.2) vs 1.2 (1.3) for eligible and ineligible patients respectively. After adjusting for age and sex, inclusion in clinical trials was associated with fewer comorbidities (OR 0.81, 95% CI 0.70, 0.94, p < 0.01). Conclusion Patients with multi-morbidity are more likely to be ineligible for SLE clinical trials. Evidence from real world studies and registers are therefore needed to fully understand the safety and effectiveness of new therapies. Our data also underscores the need to develop more pragmatic eligibility criteria for clinical trials. Disclosure S. Dyball: None. S. Collinson: None. E. Sutton: None. E. McCarthy: None. B. Parker: Consultancies; GSK, AstraZenica, UCB, Abbvie, Pfizer, BMS, Celltrion. Grants/research support; GSK, Sanofi Genzyme. I. Bruce: Consultancies; GSK, Medimmune, AstraZenica, Eli Lilly, Merck Serono, UCB, ILTOO. Member of speakers’ bureau; AstraZeneca, Medimmune, GSK, UCB. Grants/research support; GSK, Genzyme Sanofi, UCB.

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Real-world effects of ACE inhibitors and Angiotensin Receptor Blockers: protocol for an emulation study of the ONTARGET trial using electronic health records

10.1101/2021.03.15.21251990 ◽

2021 ◽

Author(s):

Paris Baptiste ◽

Angel YS Wong ◽

Anna Schultze ◽

Marianne Cunnington ◽

Johannes FE Mann ◽

...

Keyword(s):

Primary Care ◽

Clinical Trial ◽

Clinical Trials ◽

Cohort Study ◽

Treatment Effects ◽

Dual Therapy ◽

List Type ◽

Exclusion Criteria ◽

Eligibility Criteria ◽

Specific Effects

ABSTRACTIntroductionCardiovascular disease (CVD) is a leading cause of death globally, responsible for nearly 18 million deaths worldwide in 2017. Medications to reduce the risk of cardiovascular events are prescribed based upon evidence from clinical trials which explore treatment effects in an indicated sample of the general population. However, these results may not be fully generalisable because of trial eligibility criteria that generally restrict to younger patients with fewer comorbidities. Therefore, evidence of effectiveness of medications for groups underrepresented in clinical trials such as those over 75 years, from ethnic minority backgrounds or with low kidney function may be limited.The ONTARGET trial studied the effects of an angiotensin-converting-enzyme (ACE) inhibitor and an angiotensin II receptor blocker (ARB) separately and in combination on cardiovascular event reduction. Using individual anonymised data from this study, in collaboration with the original trial investigators, we aim to investigate clinical trial replicability within routinely-collected patient data. If the original trial results are replicable, we will assess treatment effects and risk in groups underrepresented and excluded from the original clinical trial.Methods and analysisWe will develop a cohort analogous to the ONTARGET trial within CPRD between 1 January 2001 to 31 July 2019 using the trial eligibility criteria and propensity score matching. The primary outcome, as in the trial, is a composite of cardiovascular death, non-fatal MI, non-fatal stroke and hospitalisation for congestive heart-failure, examined in a time-to-event analysis. If results from the cohort study fall within pre-specified limits, we will expand the cohort to include those with advanced kidney dysfunction and increase the proportion of elderly participants and those from ethnicity minority backgrounds.We will then compare the risk of adverse events and association with long-term outcomes in the clinical trial, with that seen in a directly comparable sample of those attending NHS primary care.STRENGTHS AND LIMITATIONSStrengthsLarge cohort study giving power to look at effects within subgroups underrepresented in the clinical trialAccess to individual patient level data from a landmark trial to support creation of a trial-analogous cohortNovelty of studying treatment effects of dual therapy in real-world settingsLimitationsThere may be differences between the trial population and the observational cohort due to the level of detail on inclusion/exclusion criteria provided by the trialDrug-specific effects are unlikely to be able to be investigated due to small numbers in the dual-therapy arm: class-specific effects will be studied insteadMisclassification by primary care coding may lead to inaccurate replication of trial inclusion and exclusion criteria.

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Survival in the Real World: A National Analysis of Patients Treated for Early-Stage Breast Cancer

JCO Oncology Practice ◽

10.1200/op.21.00274 ◽

2021 ◽

pp. OP.21.00274

Author(s):

Risha Gidwani ◽

Jeffrey A. Franks ◽

Ene M. Enogela ◽

Nicole E. Caston ◽

Courtney P. Williams ◽

...

Keyword(s):

Breast Cancer ◽

Clinical Trials ◽

Proportional Hazards ◽

Early Stage ◽

Patient Characteristics ◽

Cox Proportional Hazards ◽

Data Set ◽

Cox Proportional Hazards Models ◽

National Analysis ◽

Study Participants

PURPOSE: Many patient population groups are not proportionally represented in clinical trials, including patients of color, at age extremes, or with comorbidities. It is therefore unclear how treatment outcomes may differ for these patients compared with those who are well-represented in trials. METHODS: This retrospective cohort study included women diagnosed with stage I-III breast cancer between 2005 and 2015 in the national CancerLinQ Discovery electronic medical record–based data set. Patients with comorbidities or concurrent cancer were considered unrepresented in clinical trials. Non-White patients and/or those age < 45 or ≥ 70 years were considered under-represented. Patients who were White, age 45-69 years, and without comorbidities were considered well-represented. Cox proportional hazards models were used to evaluate 5-year mortality by representation group and patient characteristics, adjusting for cancer stage, subtype, chemotherapy, and diagnosis year. RESULTS: Of 11,770 included patients, 48% were considered well-represented in trials, 45% under-represented, and 7% unrepresented. Compared with well-represented patients, unrepresented patients had almost three times the hazard of 5-year mortality (adjusted hazard ratio [aHR], 2.71; 95% CI, 2.08 to 3.52). There were no significant differences in the hazard of 5-year mortality for under-represented patients compared with well-represented patients (aHR, 1.19; 95% CI, 0.98 to 1.45). However, among under-represented patients, those age < 45 years had a lower hazard of 5-year mortality (aHR, 0.63; 95% CI, 0.48 to 0.84) and those age ≥ 70 years had a higher hazard of 5-year mortality (aHR, 2.21; 95% CI, 1.76 to 2.77) compared with those age 45-69 years. CONCLUSION: More than half of the patients were under-represented or unrepresented in clinical trials, because of age, comorbidity, or race. Some of these groups experienced poorer survival compared with those well-represented in trials. Trialists should ensure that study participants reflect the disease population to support evidence-based decision making for all individuals with cancer.

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