Breast cancer screening program in canada: successes and challenges Programas de detección temprana de cáncer de mama en canadá: avances y obstáculos
Bibliographic record
Abstract
This paper describes breast screening program development in Canada and the current status of screening in Canada. Programs have been implemented in most of Canada, beginning in the late 1980's. Certain components are common to all the programs, but others, such as personal invitation letters for recruitment and clinical breast examination vary across the country. Key successes in organized breast screening in Canada include the development of a comprehensive set of screening performance indicators, which are reported on regularly, and the downward trend in mortality rates observed over the past 20 years. Challenges include the continued prevalence of opportunistic screening; the need to better manage follow-up; dealing with changing evidence; and supporting informed decision-making about screening. Approaches to breast screening are dependent on the health care services available in countries, but regardless of the approach, good evaluation is necessary. Este artículo describe el desarrollo de la detección temprana de cáncer de mama en Canadá así como la situación actual de los programas de detección de cáncer en el mismo país. En su gran mayoría, estos programas de detección han sido implementados desde comienzos de los años ochenta. Algunos elementos de estos programas representan denominadores comunes en todos ellos. Sin embargo existen otros elementos -tales como invitaciones personales para reclutamiento y exámenes clínicos de mama, que difieren dependiendo de cada jurisdicción. Algunos de los avances en los programas organizados de detección temprana de cáncer de mama en Canadá incluyen la existencia de indicadores de evaluación de desempeño, sobre los cuales se reporta de forma regular. En base a estos indicadores se puede observar una tendencia descendente en los índices de mortalidad en los últimos 20 años. Algunas de las dificultades incluyen la persistencia de detección oportunística, la necesidad de gerenciar el efectivo seguimiento de pacientes, gerenciar el constante cambio de evidencia, así como el proveer asistencia en la toma de decisiones relacionadas a la detección temprana de cáncer. Las prácticas focalizadas en mejorar la detección temprana de cáncer dependen de los servicios de salud existentes en cada país. Sin embargo e independientemente de la orientación utilizada, la necesidad de evaluar el desempeño de los programas es un elemento vital.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| 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".