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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

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Related Experiment Video

Updated: May 21, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Cancer auditing - how accurate are your data?

C Platell1, C Penter

  • 1Colorectal Cancer Unit, St John of God Hospital, Subiaco, WA, Australia. cameron.platell@uwa.edu.au

Colorectal Disease : the Official Journal of the Association of Coloproctology of Great Britain and Ireland
|June 27, 2012
PubMed
Summary
This summary is machine-generated.

Data entry for colorectal neoplasia databases shows significant variability between observers. Key areas like length of stay and recurrence time had high agreement, but others like ASA grade and emergency surgery showed notable discrepancies.

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Area of Science:

  • Colorectal Surgery
  • Database Management
  • Clinical Data Accuracy

Background:

  • Human data entry into databases is prone to errors.
  • Accurate data collection is crucial for clinical research and patient care in colorectal neoplasia.

Purpose of the Study:

  • To evaluate the concordance between two independent data collectors for a colorectal neoplasia database.
  • To identify potential sources of data inaccuracy in prospective colorectal databases.

Main Methods:

  • A colorectal research nurse and a surgeon independently collected data for new colorectal neoplasia patients over five years.
  • Twenty-three key endpoints were analyzed using κ statistic and concordance correlation coefficient to assess inter-observer agreement.

Main Results:

  • Complete concordance was observed for the number of new patients and 30-day returns to the theater.
  • Almost perfect concordance was found for most endpoints, but significant variance occurred in length of stay, American Society of Anesthesiology (ASA) grade, emergency surgery, nodal staging, and time to recurrence.

Conclusions:

  • Prospective colorectal databases exhibit data inaccuracies stemming from observer bias, interpretation challenges, and collection difficulties.
  • Addressing these data quality issues is essential for reliable colorectal neoplasia research.