Estimates of cancer burden in Umbria
Bibliographic record
Abstract
Aims and background Model-based estimates and projections of epidemiological indicators related to cancer are important tools to support public health policies and planning. The aim of the present study is to produce projections of cancer incidence, mortality and prevalence for the Umbria region (900,000 inhabitants) in central Italy. Methods The estimations were obtained by applying the MIAMOD method, a statistical back-calculation approach to derive incidence and prevalence figures starting from mortality and relative survival data. Published data from the Italian cancer registries were modeled in order to estimate regional cancer survival. Estimated incidence rates were validated with observed incidence rates obtained from the Umbria regional cancer registry. Results The most frequent cancer sites estimated were colon-rectum, prostate and breast in women, with 970, 615 and 729 new diagnoses, respectively, in 2012. The incidence rates were increasing for female lung cancer, male colorectal cancer, and melanoma. By contrast, the rates have been declining for cervix and stomach cancer. For lung cancer and prostate cancer in men and colorectal cancer in women the rates increased, reaching a peak in different periods, and then decreased. The incidence rates of breast cancer rose, reaching a plateau in the mid 2010s. Favorable mortality trends were predicted for all cancers except skin melanoma and lung cancer in women. The prevalence of cancer was increasing with the only exception of cervical cancer in women and lung cancer in men in the most recent estimation period. Conclusion The scenario found for cancer incidence and prevalence was largely influenced by screening activities, so that increasing or stable incidence rates may reflect active preventive efforts. Aging, screening, and more complex and costly treatments pose a problem of sustainability and selection of interventions to the regional oncology system. Evaluation of effectiveness of intervention and cost-benefit analyses will be important to ensure cancer control in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".