Urban-rural differences in gynaecological cancer occurrence in a central region of Italy: 1978-1982 and 1998-2002.
Bibliographic record
Abstract
Differences in gynaecological cancer incidence and mortality in the urban and rural areas of the Umbria region (central Italy) were investigated. All women with primary invasive breast cancers, uterine cervix and uterine corpus, and ovarian cancers diagnosed during the periods 1978-1982 and 1998-2002 were identified and analysed according to place of residence (either urban or rural). Mortality data were supplied by the National Institute of Statistics (ISTAT) for the period 1978 to 1982, whereas for the 1994-2002 period they were supplied by the Regional Nominative Causes of Death Registry (ReNCaM). Incident cases considered were taken from an ad hoc survey for the first period and from the Umbrian Population Cancer Registry database for the second one. For each site the age-adjusted incidence (AAIR) and mortality (AADR) rates were calculated. The expected number of rural cases was obtained from indirect standardisation with urban incidence and mortality rates of several sites. The significance of the observed expected ratios (SIRs for incidence and SMRs for mortality) and the corresponding 95% confidence intervals were based on the Poisson distribution. Urbanisation levels were established following the classification of the Italian Institute of Statistics. For all sites, excluding the ovary during the most recent period, the SIR relative to rural areas was below 1, but the rates were statistically significant only for breast cancer in both periods (SIR 0.81, 95% CI 0.74-0.88 and SIR 0.82, 95% CI 0.77-0.88, respectively) and for cervix uteri in the first period (SIR 0.77, 95% CI 0.59-0.94). The lower breast cancer incidence in the rural area could also be due to lesser compliance with screening procedures which, up until 2002, were not provided in the form of mass-screenings throughout the region by the Regional Health Department. These results underscore the need for continued efforts to provide preventive health services to medically underserved women throughout Umbria, including rural communities. Underutilisation of preventive healthcare services may result in failure to identify healthcare problems that might be successfully managed with medication or lifestyle changes, as well as missed opportunities to prevent potentially life-threatening diseases.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".