Printed in U.S.A. AGE DEPENDENCE OF COHORT PHENOMENA IN BREAST CANCER MORTALITY IN THE UNITED STATES
Bibliographic record
Abstract
Breast cancer mortality has increased in most parts of the world, and many explanations have been postulated. In this paper, the authors examined the evolution of mortality rates for white and nonwhite females in the United States from 1950-1979. Using both graphic techniques and Poisson regression models, they found that there has been strong modification of apparent cohort effects by age. For both white and nonwhite females, they observed an increase in mortality rates limited to the postmenopausal ages. breast neoplasms; mortality; regression analysis Using rates for white females from the Connecticut Cancer Registry, MacMahon (1, 2) reported that time trends in breast cancer incidence were occurring in a pat-tern that indicated an important contribu-tion of attributes related to the year of birth; he noted that incidence rates were slowly increasing, although younger age groups experienced a leveling off. Stevens et al. (3), examining data from the United States, Canada, England and Wales, and Japan, concluded that the temporal evolu-tion of breast cancer mortality has been similar among these countries. In all of the populations that these investigators re-viewed, the cohort effect parameters fell until the birth cohort born around 1900, followed by a steady increase thereafter. Received for publication May 5, 1986, and in final form December 24, 1986. Abbreviation: GLIM, generalized linear interactive modeling.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".