Importance of quality in breast cancer screening practice – a natural experiment in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: Regular breast cancer screening is a widely used cancer prevention strategy. Important quality indicators of screening include cancer detection rate, false positive rate, benign biopsy rate and post-screen invasive cancer rate. We compared quality indicators of community radiology clinics to those of 'Screen Test', which feature centralised batch reading and quality control processes. Both types of providers operated under a single provincial Breast Cancer Screening Programme. SETTING: Community radiology clinics are operated by independent fee-for-service radiologists serving large and small communities throughout the Canadian province of Alberta. Launched by the provincial cancer agency, the Screen Test operates two physical clinics serving metropolises and mobile units serving remote regions. Eligible women may self-refer to any provider for screening mammography. PARTICIPANTS: Women aged 50 to 69 years who had at least one screening mammogram between July 2006 and June 2010 in Alberta were included. Women with missing health region information or prior breast cancer diagnosis were excluded. RESULTS: A total of 389 788 screening mammograms were analysed, of which 12.7% were performed by Screen Test. Compared with Screen Test during 2006 to 2008, community radiology clinics had a lower cancer detection rate (3.6 vs 4.6 per 1000 screens, risk ratio (RR): 0.81, 95% CI: 0.67 to 0.98) and a much higher false positive rate (9.4% vs 3.4%, RR: 2.72, 95% CI: 2.55 to 2.90). Most other performance indicators were also better in Screen Test overall and across all health regions. These performance indicators were similar during 2008 to 2010, showing no improvement with time. CONCLUSIONS: Screen Test has a quality assurance process in place and performed significantly better. This provides empirical evidence of the effectiveness of a quality assurance process and may explain some of the large differences in breast cancer screening indicators between provinces and countries with formal programmes and those without.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".