Comparison of nonbreast cancer incidence, survival and mortality between breast screening program participants and nonparticipants
Bibliographic record
Abstract
Comparisons of cancer mortality between users and nonusers of screening are potentially biased because of the effects of self-selection. Previous studies of breast screening have found that individuals likely to participate have lower breast cancer mortality than those unlikely to participate. This study compares the incidence, survival and mortality for all cancer types other than breast between participants and nonparticipants in a service screening mammography program. British Columbian females having their first mammogram between the ages of 40 and 79 and the years 1988 and 2004 were identified as a cohort of "participants". Person-years of follow-up of participants were aggregated by age and year. Nonparticipant person-years were obtained by subtraction from the total female population. Cancer diagnoses other than breast were identified for participants and nonparticipants. Age, calendar year, and income adjusted relative risks of cancer incidence were estimated from generalized additive models with Poisson errors. Hazard ratios were estimated by Cox regression. Observed cancer mortality in participants was compared with expected mortality generated from nonparticipant incidence and survival rates. Incidence rates of cancer showed a mixed relationship with some elevated, some decreased and others similar to nonparticipant rates. Cancer survival was higher among participants for most cancer types, with an overall hazard ratio of 0.76 (0.73-0.79). Observed mortality in participants was less than expected for most cancers, with an overall mortality ratio of 0.60 (0.58-0.62). The general cancer experience of screening program participants is different from that of the general population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".