SCDM CCDM Exam With Confidence Using Practice Dumps

When efficacy data arenot covered by existing CDISC SDTM domains, thefirst resourcethe Data Manager should consult is theCDISC Therapeutic Area Implementation Guide (TAIG)for that therapeutic field.

According to theGCDMP (Chapter: Standards and Data Mapping), CDISC’s Therapeutic Area User Guides (TAUGs) and Implementation Guides providestandardized data structures, variable definitions, controlled terminology, and implementation examplesfor specific diseases or therapeutic areas. These guides ensure consistency across studies, promote interoperability, and align data collection with regulatory submission expectations.

Consulting other sponsors’ forms or external registries (options A and C) can be informative but do not provide authoritative CDISC-compliant standards. SNOMED terms (option B) address medical terminology, not structural data domain definitions.

Therefore,Option Dis correct—CDISC TA Implementation Guidesare the recognized primary reference when extending or designing SDTM-compliant CRFs.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Standards and Data Mapping, Section 4.2 – Use of CDISC Standards

CDISC Therapeutic Area User Guides (TAUGs) – Implementation Guidance for Domain Extension

FDA Data Standards Catalog – CDISC Therapeutic Area Standards

If a blood sample is drawndistal (downstream)from a saline infusion site, it may becomecontaminated with saline, leading toabnormal laboratory results. Saline contains a high concentration of sodium chloride, which artificially elevates sodium while diluting other blood components.

Therefore, such samples would display:

Very high sodium levels, and

Abnormally low levelsof other analytes (e.g., proteins, glucose, potassium).

This abnormal pattern (option B) is a classic indicator ofsaline contamination.

Per theGCDMP (Chapter: Data Validation and Cleaning),cross-variable consistency checksare critical for identifying biologically implausible patterns, such as this one, which indicatepre-analytical errorsrather than true physiological changes.

Hence,option Baccurately describes the data signature of a contaminated blood draw.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 6.2 – Logical and Consistency Checks for Laboratory Data

ICH E6(R2) GCP, Section 5.1.1 – Data Quality and Biological Plausibility Checks

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Detecting Laboratory Anomalies

Aself-evident correction (SEC)refers to a data correction that is obvious, logical, and unambiguous — such as correcting an impossible date (e.g., 31-APR-2024) or standardizing a known abbreviation (e.g., “BP” to “Blood Pressure”). According to theGood Clinical Data Management Practices (GCDMP), SECs can be applied by data management stafffollowing pre-approved conventions defined in the Data Management Plan (DMP).

The DMP should explicitly describe the criteria for SECs, including the types of errors eligible for this correction method, the required documentation, and the communication procedure to inform the investigative site. The process must maintainaudit trail transparencyand ensure that all changes aretraceable and justified.

Options A and B suggest unauthorized or informal change procedures, which violate audit and compliance standards. Option C is too restrictive, as it prevents the efficient correction of non-clinical transcription or formatting errors.

Therefore,option Dis correct:“Self-evident changes may be made per the listed conventions and documented to the investigative site.”This approach aligns with CCDM expectations for balancing efficiency, accuracy, and regulatory compliance.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 6.2 – Self-Evident Corrections

FDA 21 CFR Part 11 – Electronic Records; Audit Trails and Traceability Requirements