Meaningful reporting in primary care: An evaluation of the screening activity report.
Bibliographic record
Abstract
173 Background: Cancer Care Ontario (CCO) developed the ColonCancerCheck Screening Activity Report (CCC SAR) as a supplementary tool for primary care physicians (PCPs) who are part of a patient enrolment model in Ontario, Canada to support them in increasing their colorectal cancer screening rates and appropriate follow-ups. The report provides PCPs with a summary of their eligible patients’ colorectal cancer screening-related history and was designed with the intent of supporting both population health management and opportunistic screening. Delivered through an online platform, the SAR offers PCPs access to data to facilitate quality cancer screening practices aligned with CCO’s evidence-based clinical guidelines. Methods: The report leverages provincial datasets to summarize screening activities on a per-patient level and actionable follow-up recommendations based on CCO’s clinical guidelines. To evaluate the impact of the reports on colorectal screening participation rates, a Generalized Estimating Equation model was used. Results: Results on the report’s impact on colorectal cancer screening participation rates from the first release (February 2013) are promising. By identifying for PCPs which patients are overdue for screening, the SAR demonstrated a modest and statistically significant 6% increased likelihood of patients being screened using a Fecal Occult Blood Test (FOBT) if their PCP was registered to access the online report, compared to patients of unregistered PCPs. The impact to screening increased to 25% when comparing registered PCPs who logged in and viewed their SAR to registered PCPs who did not login; however, this analysis is at a greater risk of a volunteer bias. Such PCPs may be more likely to screen their patients independent of viewing their SAR. Conclusions: By equipping PCPs with patient-level data grounded in CCO’s evidence-based clinical guidelines, the SAR is innovative in its potential to increase screening rates and the appropriate follow-up of abnormal results. The successful launch of two online CCC SARs, sharing meaningful colorectal cancer screening data to frontline providers, has driven the report’s expansion to include breast and cervical cancer screening data in spring of 2014.
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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 | 0.251 | 0.553 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".