A62 POST COLONOSCOPY COLORECTAL CANCERS IN ALBERTA. A PROCESS FOR IDENTIFYING TRUE CASES
Bibliographic record
Abstract
Determining Post Colonoscopy Colorectal Cancer (PCCRC) rates is one of the most important measures of colonoscopy quality. Most commonly, PCCRCs are the result of technical factors surrounding the colonoscopy such as inadequate bowel preparation, incomplete examination, missed early lesions and failure to adhere to follow-up guidelines. As these factors are amenable to quality interventions, we set out to identify PCCRC cases from a population perspective with a view to calculating incidence rates. Our objective was to develop a framework for data gathering and analysis in order to identify PCCRC cases and rates in Alberta in order to obtain a clearer understanding of the underlying causes of PCCRC where potential quality interventions might be applied. This was a retrospective population based review of all cases of colorectal cancer (CRC) diagnosed in Alberta in 2013. Data from the Alberta Cancer Registry (ACR) was linked to the Discharge Abstract Database (DAD), the National Ambulatory Care Reporting System (NACRS) and Alberta Ambulatory Care Reporting System (AACRS) databases to determine the timing of antecedent colonoscopies. We defined a PCCRC as a case identified in the ACR with ICD-10 codes for colorectal cancer with an antecedent colonoscopy greater than 6 months but less than 3 years prior to the diagnosis of CRC. Individual chart reviews were carried out to exclude high-risk groups such as IBD or genetic syndromes and to determine lesion location. Before a PCCRC rate could be calculated, we identified that the initial data linking process provided a number of cases that required further in depth review to determine if they met inclusion and exclusion criteria. Subsequently, through an iterative process of chart review, we developed a decision analysis framework (see Figure1), that provided a rational basis for case exclusion as well as systematic categorization of PCCRC root causes. Our analysis also identified areas for future quality improvement initiatives: such as the failure to arrange follow-up after poor bowel preparation or advanced lesions. We also identified cases where access to timely care resulted in the development of a PCCRC. Attempts to identify cases of PCCRC through database linkage identifies cases that require in depth analysis to determine eligibility. We have developed an algorithm that provides a rational basis for case exclusion as well as systematic categorization of PCCRC root causes. None
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".