Linking lab, program, and administrative data to provide comprehensive colorectal cancer screening status of patients to primary care providers in Calgary, Alberta
Bibliographic record
Abstract
IntroductionColorectal cancer (CRC) screening is associated with significant reductions in burden, mortality and cost. Primary care providers in Alberta do not have access to integrated CRC testing histories for patients. Providing this information will support CRC screening among patients at average and high risk, follow-up of abnormal tests, and surveillance. Objectives and ApproachCalgary Laboratory Services, Colon Cancer Screening Centre, Alberta Cancer Registry, and endoscopy data were linked to create a comprehensive CRC screening history at the patient level. Based on screening histories and the current Clinical Practice Guideline, an algorithm was created to determine CRC screening statuses with the aim of providing accurate screening rates when linked to primary care provider patient panels. Results from the linkage are designed to be incorporated into clinic and EMR workflow processes to support adherence to evidence-based screening recommendations at the point of care. ResultsA comprehensive assessment of screening status was determined by integrating Fecal Immunochemical Test (FIT) and colonoscopy data. Among a sample cohort, patients were identified as being due for screening with FIT, requiring follow-up for a positive FIT test, or requiring appropriate surveillance for a positive-screen or abnormal colonoscopy findings. A summary report, actionable list, and resources were developed to convey findings. The summary report displayed CRC screening rates for a provider’s panel. The actionable list provided CRC screening statuses for each patient aged 40 to 84 indicating patients due for screening with FIT, for follow-up of positive FIT, or for surveillance colonoscopy. The resources were developed to support quality improvement for colorectal cancer screening for patients. Conclusion/ImplicationsThe data linkages and algorithm provide comprehensive CRC screening, follow-up, and surveillance information that could support guideline-adherent screening, increase screening rates, reduce duplication or unnecessary testing, and provide primary care providers with timely and robust information to support clinical decisions for individuals inside and outside of the target screening population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".