Bibliographic record
Abstract
Organized screening for breast cancer in Canada began in 1988 and has been implemented in all provinces and 2 of the 3 territories. Quality initiatives are promoted through national guidelines which detail best practices in various areas, including achieving quality through a client-service approach, recruitment and capacity, retention, quality of mammography, reporting, communication of results, follow-up and diagnostic workup, and program evaluation; it also offers detailed guidelines for the pathological examination and reporting of breast specimens. The Canadian Breast Cancer Data Base is a national breast cancer screening surveillance system whose objective is to collect information from provincial-screening programs. These data are used to monitor and evaluate the performance of programs and allow comparison with national and international results. A series of standardized performance indicators and targets for the evaluation of performance and quality of organized breast cancer screening programs have been developed from the data base. Although health care is a provincial responsibility in Canada, the collective reporting and comparison of results both nationally and internationally is beneficial in evaluating and refining both screening programs and individual radiologist performance. The results of Canadian performance indicators compare favourably with those of other well-established international screening programs. There are variations in performance indicators across the provinces and territories, but these differences are not extreme.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".