Mortality due to cervical cancer, 1996-2011, Santa Catarina, Brazil
Bibliographic record
Abstract
Population-based quantitative study. This study's objectives were to examine the influence of regions, age, and time on cervical cancer mortality. We analyzed cervical cancer mortality rates in the State of Santa Catarina, Brazil between 1996 and 2011. Data were obtained from the Mortality Information System from the Brazilian Ministry of Health in 2013. Data were analyzed based on descriptive epidemiology. Cervical cancer mortality rates ranged from 3.6 to 5.0/100,000 women. Rates were higher among older women, with the highest rates after the age of 70. Women's awareness regarding the importance of the Pap smear test in their health checkups may differ across ages and regions of residence. The frequency of Pap smears should change due to the trend of mortality rates observed over time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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; both teacher heads agree on what is shown here.
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".